This paper presents a method for optimizing the gains of a fuzzy logic controller (FLC) for an unmanned aerial vehicle (UAV) using a genetic algorithm (GA). The authors model, design, and implement a typical fuzzy controller using Newton-Euler methodology. The control gains are then optimized using a GA to minimize energy consumption. The GA tunes the gains to meet design parameters, improving trajectory tracking accuracy. Tests conducted in the Matlab-Simulink environment show that the tuned controller reduces tracking errors from 30% in some tasks and enables the completion of trajectories that were previously impossible without tuning. The results highlight the effectiveness of the GA in optimizing the FLC for UAVs, demonstrating improved performance in both simple and complex trajectory tracking tasks.This paper presents a method for optimizing the gains of a fuzzy logic controller (FLC) for an unmanned aerial vehicle (UAV) using a genetic algorithm (GA). The authors model, design, and implement a typical fuzzy controller using Newton-Euler methodology. The control gains are then optimized using a GA to minimize energy consumption. The GA tunes the gains to meet design parameters, improving trajectory tracking accuracy. Tests conducted in the Matlab-Simulink environment show that the tuned controller reduces tracking errors from 30% in some tasks and enables the completion of trajectories that were previously impossible without tuning. The results highlight the effectiveness of the GA in optimizing the FLC for UAVs, demonstrating improved performance in both simple and complex trajectory tracking tasks.