The optimization approach has been essential in improving many chemical processes and providing significant improvements to many difficult processes. It often falls under two categories which are single-objective optimization and multi-objective optimization. These methods involve a process-derived objective function that can be maximized or minimized. Multi-objective optimization was considered in this research to optimize the biomethane liquefaction process. Finding a trade-off between two or more objective functions that might have an impact on one another is the central goal of this methodology. These conflicting objectives result in trade-offs that lead to the Pareto front solution, which is a set of feasible solutions. Recently, the biomethane liquefaction process became an interesting process to look at in a time where the demand for energy from all its sources is rising significantly. Liquified biomethane (LBM) is one type of biogas or bioenergy source that will be highly needed. The issue is that the liquefaction of biomethane gas is considered an energy-intensive process. In this project, multi-objective optimization is used to find a trade-off among the selected objective functions. In LBM optimization, specific energy consumption (SEC), total energy of the system, heat transfer coefficient (UA), exergy efficiency, and exergy destruction are the selected objective functions with the associated design variables. The optimization has been achieved through a simulation stage in Aspen HYSYS v12.1 and using MATLAB as an interface platform. The selected algorithm is NSGA-II, and a few cases have been obtained with Pareto optimal fronts based on the requirements. The obtained results indicated a significant enhancement in the values of the optimized objective functions in comparison with the base-case values.