In this chapter, we propose the use of rough sets to improve the approximation provided by a multi-objective evolutionary algorithm. The main idea is to use this sort of hybrid approach to approximate the Pareto front of a multi-objective optimization problem with a low computational cost (only 3000 fitness function evaluations). The hybrid operates in two stages: in the first one, a multi-objective version of differential evolution is used as our search engine in order to generate a good approximation of the true Pareto front. Then, in the second stage, rough sets theory is adopted in order to improve the spread of the solutions found so far. To assess our proposed hybrid approach, we adopt a set of standard test functions and metrics taken from the specialized literature. Our results are compared with respect to the NSGA-II, which is an approach representative of the state-of-the-art in the area.