Skill composition is a growing area of interest within reinforcement learningresearch. This approach promotes efficient use of knowledge and represents arealistic, human-like style of learning. Existing work has demonstrated how simple skills can be composed using Boolean operators to solve new, unseen taskswithout further learning. However, this approach assumes that the learned value functions for each atomic skill are optimal, an assumption which is violated in most practical cases. We propose a method that instead learns operators forcomposition using evolutionary strategies. We empirically verify our approach in tabular and high-dimensional environments. Results demonstrate that our approach outperforms existing composition methods when faced with learned, suboptimal behaviours, while also promoting robust agents and allowing for transfer between domains.