In this paper the influence of control parameters to the search process of previously introduced Generalized Differential Evolution is empirically studied. Besides number of generations and size of the population, Generalized Differential Evolution has two control parameters, which have to be set before applying the method for solving a problem. The effect of these two control parameters is studied with a set of common bi-objective test problems and performance metrics. Objectives of this study are to investigate sensitivity of the control parameters according to different performance metrics, understand better the effect and tuning of the control parameters on the search process, and to finnd rules for the initial settings of the control parameters in the case of multi-objective optimization. It seems that in the case of multi-objective optimization, there exists same kind of relationship between the control parameters and the progress of population variance as in the case of single- objective optimization with Differential Evolution. Based on the results, recommendations choosing control parameter values are given.