Feature selection for ensembles has shown to be an effective strategy for ensemble creation. In this paper we present an ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm. The first level performs feature selection in order to generate a set of good classifiers while the second one combines them to provide a set of powerful ensembles. The proposed method is evaluated in the context of handwritten digit recognition, using three different feature sets and neural networks (MLP) as classifiers. Experiments conducted on NIST SD19 demonstrated the effectiveness of the proposed strategy.