We present Evolutionary Imitation Learning (EvIL), a general approach to imitation learning (IL) able to predict agent behaviour across changing environment dynamics. In EvIL, we use Evolution Strategies to jointly meta-optimise the parameters (e.g. reward functions and dynamics) fed to an inner loop reinforcement learning procedure. In effect, this allows us to inherit some of the benefits of the inverse reinforcement learning approach to imitation learning while being significantly more flexible. Specifically, our algorithm can be applied with any policy optimisation method, without requiring the reward or training procedure to be differentiable. Our method succeeds at recovering a reward that induces expert-like behaviour across a variety of environments, even when the environment dynamics are not fully known. We test our method's effectiveness and generalisation capabilities in several tabular environments and continuous control settings and find that it outperforms both offline approaches, like behavioural cloning, and traditional inverse reinforcement learning techniques.