In this paper an Evolutionary Algorithm, Differential Evolution, and its extension for constrained multi-objective (Pareto-)optimization, Generalized Differential Evolution, are described. Performance of Generalized Differential Evolution is tested with a set of five benchmark multi-objective test problems. Suitable control parameter values for these test problems are surveyed and the results are compared numerically with other multi-objective evolutionary algorithms including the Strength Pareto Evolutionary Algorithm and the Non-dominated Sorting Genetic Algorithm. Several metrics commonly used in the literature are applied to measure convergence to the Pareto-optimal front and diversity of the obtained solution. The results are suggesting that the performance of Generalized Differential Evolution is well comparable to the performance of the compared multi-objective evolutionary algorithms.