In this paper a new evolutionary multi-objective algorithm (GAR-SD) for Subgroup Discovery tasks is presented. This algorithm can work with both discrete and continuous attributes without the need for a previous discretization. An experimental study was carried out to verify the performance of the method. GAR-SD was compared with other subgroup discovery methods by evaluating certain measures (such as number of rules, number of attributes, significance, support and confidence). For Subgroup Discovery tasks, GAR-SD obtained good results compared with existing algorithms.