Using an incremental multi-objective evolutionary algorithm and the ModNet encoding, we generated working neuro-controllers for target-following behavior in a simulated flapping-wing animat. To this end, we evolved tail con- trollers that were combined with two closed-loop wing-beat controllers previously generated, and able to secure straight flight at constant altitude and speed. The corresponding results demonstrate that a wing-beat strategy that consists in continuously adapting the twist of the external wing panel leads to better maneuvering capabilities than another strategy that adapts the beating amplitude. Such differences suggest that further improvements in flying control should bet- ter rely on some sort of automatic incremental evolution procedure than on any hand-designed decomposition of the problem.