Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this paper we show the first application of the Behaviour Tree framework to a real robotic platform using the Evolutionary Robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behaviour as compared to the traditional Neural Network formulation. As a result, the behaviour is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-gram DelFly Explorer flapping wing UAV equipped with a 4-gram onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the learnt behaviour in simulation 88% and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.