Saving energy is a critical issue for mini and micro-UAVs. We used tools rooted in the ’animat’ approach to generate energy saving behaviors for a glider robot. The connection weights of feed-forward neural networks were optimized by evolutionary algorithms to exhibit “soaring” behaviors, i.e. behaviors that capitalize on aerological conditions to extract energy from the environment, focusing on thermal and slope wind exploitation. Thermal soaring with spiral trajectories and slope soaring with eight-shaped trajectories were thus exhibited. The optimization criterion used for thermal soaring was the average altitude gain. For slope soaring, an additional criterion forced the glider to remain in a limited area. These criteria were high-level specifications of the desired behaviors and did not include any direct description of the strategy needed to get them.