The application of chemicals in agricultural areas is of crucial importance for crop production. The use of aircrafts is becoming increasingly common in carrying out this task mainly because of their speed and effectiveness. Nonetheless, some factors may reduce the yield, or even cause damage, like areas not covered in the spraying process or overlapped spraying areas. Weather conditions add further complexity to the problem. Sets of control rules, to be employed in an autonomous Unmanned Aerial Vehicles (UAV), are very hard to develop and harder to fine-tune to each environment characteristics. Hence, a fine-tuning phase must involves the parameters of the algorithm, due to the mechanical characteristics of each UAV and also must take into account the type of crop being handled and the type of pesticide to be used. In this paper we present an evolutionary algorithm to fine-tune sets of control rules, to be employed in a simulated autonomous UAV. We describe the proposed architecture and investigations about changing in the evolutionary parameters. The results show that the proposed evolutionary method can fine-tune the parameters of the UAV control rules to support environment and weather changes in the simulated environment, encouraging the deployment of the system with real hardware.