Due to the complicated nature of the geographically distributed assets (GDA), team planning and scheduling problem of GDA systems is receiving increasing attention in recent research. Many approaches have been introduced to solve this complex optimization problem. However, most of the introduced optimization models in the literature have considered this problem in its simple form without involving realistic features such as precedence constraints, temporal dimension, and multi-depots, which leads to reducing the effectiveness of applying these models in real-life systems. The proposed model in this study aims to determine the best number of teams and their schedule over a planning horizon with the objective of minimizing the total associated cost. The model is formulated as a mixed integer linear programming (MILP) model based on the formulation's characteristics of a special variant of vehicle routing problem (VRP) which is multi-depot multi-period vehicle routing problem (MDMPVRP). The model also includes realistic characteristics of the problem including, maintenance teams' time window, maintenance activities dependency constraint, and activities release date. This problem is classified as NP-hard problem, therefore, a metaheuristic of a hybrid genetic algorithm and simulated annealing algorithm is proposed to solve large instances of the integrated problem of team planning and maintenance scheduling of GDA. The experimental results of examining the performance of the exact method and the metaheuristic solution method are reported in terms of solution quality and time. The model is also demonstrated by a real-life case study of geographically distributed assets of one of the leading companies in energy sector in Oman. The results showed the effectiveness of adopting the proposed optimization model in solving this study's problem by being able to propose solutions that have an average difference of 39% less than the actual paid cost and 94% faster than the actual time spent for preparing the maintenance plan.