Simulation optimization has received considerable attention due to the increased growth of manufacturing networks as well as global competition in the industry. Multi-objective Evolutionary Algorithms are developed to tackle these multi-objective optimization problems. Replication of simulation instances are required so that the evolutionary algorithms can determine the optimal solutions with high confidence. The current process of determining the number of replications is only empirically selecting an arbitrarily value. Thus, the main goal of this project is to implement an algorithm to determine the number of replications required dynamically. A multi-objective computing budget allocation (MOCBA) procedure is chosen as it can determine the allocation according to the performance of the simulation. It is important to examine whether MOCBA sacrifices performance in order to obtain savings in computing budget. Therefore the performances of multi-objective evolutionary algorithms (MOEAs) integrated with the MOCBA procedure is compared against MOEAs integrated with Uniform Computing Budget Allocation (UCBA). Due to the time consuming computer simulations, mathematical test problems are used instead. A varying amount of noise is applied at the objective functions to test the performance and adaptability of the different evolutionary algorithms and computing budget allocation. After studying the algorithm with theoretical test problems, a case study on reverse logistics is used for evaluation. The importance of the reverse logistics network is often neglected by the companies in denial of the cost involved. An effective design of a closed-loop forward supply chain and reverse maintenance network can help to reduce cost and bring more value to the products. Therefore using the simulation model of this network is an effective way to illustrate the performance of the integrated evolutionary algorithm with MOCBA. Bachelor of Engineering (Computer Engineering)