Multi objective optimization (MOO) is a very efficient technique that is used to solve complex real life problems and it has been employed in different fields. Several algorithms can be used to carry out MOO problems, but evolutionary algorithms (EA's) like non dominated sorting genetic algorithm-II (NSGA-II) and multi-objective differential evolution (MODE) proved their successfulness in solving complex problems. Although these algorithms are efficient, but there is always a potential to improve their speed of convergence by combining them with a local search method (hybridization). One of the fields, which EA's were used in, is chemical engineering as they were employed to solve their complex problems for more than one objective. Processes like lysine fermentation, sulfuric acid production, acid gas sweetening and natural gas dehydration are some of the chemical processes that can be improved with MOO from several aspects (economic, environmental, operation, safety and thermodynamic). In this work, a new hybridization technique is proposed to improve the performance of MODE algorithm in terms of speed and convergence. The proposed hybrid MODE-Dynamic Local Search (HMODE-DLS) is evaluated and validated by solving benchmark test problems (both constrained and non constrained) using MODE and HMODE-DLS. To compare between the two algorithms, five performance metrics are calculated, which are convergence (Conv), spread (SP), generational distance (GD), spacing (SPC) and hypervolume ratio (HVR). Mean and standard deviation (std) values for the performance metrics are reported and the best in each category is highlighted. The Conv metric results of the new hybrid MODE is compared with other reported ones. Additionally, the effect of local search probability is studied for selected problems. In general, HMODE-DLS performance outshines, in terms of convergence and robustness, compared to other tested algorithms. HMODE-DLS is, generally, faster and its results are of improved quality compared to MODE algorithm. NSGA-II is used to solve different MOO cases in industrial lysine bioreactor, sulfuric acid production, acid gas sweetening and dehydration. The covered objectives are economical (like profit, production cost, etc), safety (damage index), environmental (like acid gas and carbon dioxide emissions, etc.), thermodynamic (exergy destruction) and operational (like yield, productivity, water content, etc.). From each process, two cases are selected to be solved with another three algorithms (including HMODE-DLS). Gas dehydrating process was not considered for MOO previously, while the acid gas sweetening and sulfuric acid production processes are solved in this work with respect to various new objectives those were not considered in earlier studies. On the other hand, industrial lysine bioreactor is revisited in this work looking for a better results using the newly developed algorithm (HMODE-DLS). A real plant dehydration process is considered for retrofitting and MOO. The optimization is conducted for the original and the retrofitted processes. The studied MOO cases are two and three objective problems. Results showed that in each case, specific decision variables affect the results and cause the conflict between the objectives. The main decision variable, which is affected by the retrofitting is the temperature of the reboiler as it converged to values higher than 464.15 K in the original process configuration. However, in the retrofitted configuration, this value is reduced to 440.38 K. HMODE-DLS, which is used to solve some cases in the process, showed its effectiveness as it converged to the desired Pareto front. The three objective case studies solved offered an improvement (with respect to the third objective) compared to the two objective cases, which make their results more practical to be implemented in the industry. An acid gas removal process that utilizes dimethyl ethers of polyethylene glycol (DEPG) as a solvent is simulated with ProMax V4.0 to carry out MOO study with, mainly, NSGA II. The considered objectives are environmental, economic, safety, process and thermodynamic-based. Several two and three objective cases were conducted and compared. Removing sulfur can be improved with low temperature and high pressure operating conditions. Increasing the flow of the gas increases the profit, but it adversely influences safety and environmental objectives. Increasing the rate of sulfur removal involves consuming lots of energy, which in turn increases the operating cost and reduces the profit. Exergy destruction high values are encountered when high sulfur removal, methane recovery or profits are needed. When HMODE-DLS is used to solve some cases with other algorithms, it was able to achieve the needed Pareto front. Two MOO cases in an industrial lysine bioreactor at different feeding conditions are solved. Case 1 considers maximizing lysine's productivity and yield. While case 2 studies the maximization of productivity along with minimization of total operating time. In all cases, theoretical and industrial, HMODE-DLS showed a better performance with a better quality Pareto set of solutions. The Pareto front of case 1 found by HMODE-DLS is compared with a recent study trade-off, and the current non-dominated solutions values are found to be improved. For case 2, the switching time from fed-batch to batch is found to be the key decision variable. A sulfuric acid production plant that is simulated with Aspen Plus is considered for MOO using EMOO with NSGA-II. Several objectives are taken into consideration like profit, GWP, acidic emissions, I, etc. Results showed that air flow rate and production amount are the key decision variables those are causing the conflict between the objectives. It can be said that there is a potential for improving such sulfuric acid process and the results are useful to bring a deeper understanding. Similar to previous process, HMODE-DLS results are comparable to MODE results as they are able to achieve the desired Pareto, which is not the case with I-MODE, improved MODE, and NSGA-II.