In recent years, an increasing amount of research has been focused on feature selection techniques. These techniques rely on an idea that by selecting the most discriminant features, it may reduce the number of features and increase the recognition. Instead of using a feature selection technique which has been widely used in multi objective evolutionary approaches for ensemble generating, this paper presents a new multi objective evolutionary algorithm based on the NSGA II which automatically preserves diversity and also covers problems with lower dimensional feature spaces in which using feature selection technique may lead to ambiguous subspaces. After creating classifiers based on the amount of error created for each class, another multi-objective genetic algorithm was used to combine them and to produce a set of powerful ensembles. Comprehensive experiments demonstrate the effectiveness of the proposed strategy.