Micro aerial vehicles have shown promising use to further automate food production in greenhouses recently. Compared to conventional multirotor drones, flapping-wing drones offer safe and robust operation around plants due to their soft, slowly-moving wings. Their limited sensing and computational capabilities, however, prohibit the use of map-based navigation methods. To compensate for individual shortcomings, swarming ensures scalability and redundancy. This work proposes a hardware setup combining time-of-flight (ToF) and ultra-wideband (UWB) sensing and explores the artificial evolution of behaviour trees as a reactive planning strategy. Genetic programming, paired with CMAES fine-tuning was able to improve a human-designed exploration strategy by 50%. Neuroevolution has been investigated to encourage emergent swarming behaviours, but requires further experimentation in combination with behaviour trees. The solution obtained in simulation can be readily ported to hardware, but a reality gap in performance persists. These findings contribute to the development of lightweight, scalable aerial systems for autonomous greenhouse monitoring.