The generation of robot controllers for a task requiring a sequence of elementary behaviors is still a challenge. If these behaviors are known, intermediate steps can be given to help bootstrap the search, thus leading to task decomposition or incremental approaches. The goal of this paper is to propose an alternative, within which such behaviors do not need to be known. The proposed approach relies on a classical multi-objective evolutionary algorithm and consists in designing objectives dedicated to the enhancement of evolutionary search abilities. These objectives are to be used in addition to performance objectives rewarding the efficiency, robustness, or whatever aspect a robot designer might be interested in. Two different kinds of objectives are proposed, tested and compared on a ball collecting problem. Both rely on states that can be directly extracted from the sensors and are completely independent from the genotype and phenotype. They show promising results, even with a simple direct neural network encoding.