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In this paper an extension of Generalized Differential Evolution for constrained multi-objective (Pareto-)optimization is proposed. The proposed extension adds a mechanism for maintaining extent and distribution of the obtained non-dominated solutions approximating a Pareto front. The proposed extension is tested with a set of five benchmark multi-objective test problems and results are numerically compared to known global Pareto fronts and to results obtained with the elitist Non-Dominated Sorting Genetic Algorithm and Generalized Differential Evolution. Results show that the extension improves extent and distribution of solutions of Generalized Differential Evolution.