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The optimization of multi objective problems is currently an area of important research and development. The importance of type of problems has allowed the development of multiple metaheuristics for their solution. To determine which multi objective metaheuristic has the best performance with respect to a problem, in this article an experimental comparison between two of them: Sorting Genetic Algorithm No dominated-II (NSGA-II) and Multi Objective Particle Swarm Optimization (MOPS) using ZDT test functions is made. The results obtained by both algorithms are compared and analyzed based on different performance metrics that evaluate both the dispersion of the solutions on the Pareto front, and its proximity to it.