The field of swarm robotics studies multi-robot systems, emphasising decentralised and self-organising behaviours that deal with limited individual abilities, local sensing and local communication. A robotic system needs to be flexible to environmental changes, robust to failure and scalable to large groups. These desired features can be achieved through collective behaviours such as aggregation, synchronisation, coordination and exploration. We aim to analyse these emerging behaviours by applying an evolutionary approach to a specific robotic system, called the Kilobot, in order to learn behaviours. If successful, not only would the cost and computation time for evolutionary computation in mobile robotics decrease, but the reality-gap could also narrow.