In today's competitive market, sourcing is one of the most important strategic decisions of organizations. This problem by considering such factors as transportation, quality and production capacity, selects suppliers for long-term cooperation. Inventory control tactical decisions and selection of optimal inventory policy have a great impact on the cost because many organizations have a tremendous amount of capital in the form of inventory. So far, little research has been done in the area of integrated sourcing and inventory policy and systematic approach to this issue has not yet been considered. In this study, we sought to fill this research gap. In real world problems, it is difficult to calculate the exact cost of inventory shortages. To resolve this problem, shortage number was considered as a separate goal. Also, due to the probabilistic nature of the demand of plants, the objective functions cannot be calculated with conventional methods, so simulation was used to estimate the fitness of objective functions. The issue raised in this study is NP-Hard, so multi-objective meta-heuristics were used for finding the optimum solutions. Then six test problems were developed from small to large. The quality of the Pareto approximation obtained from the NSGA-II and MOPSO algorithms were evaluated by six criteria. Results showed that the solutions generated by the NSGA-II algorithm, the higher the quality.