This research presents a fuzzy logic controller for an Unmanned Aerial Vehicle (UAV) with gains optimized using a genetic algorithm. The study focuses on adjusting the gains of a fuzzy controller through a metaheuristic algorithm to improve trajectory tracking performance. A typical fuzzy controller was modeled, designed, and implemented using a mathematical model derived from the Newton-Euler methodology. The control objective is to minimize energy consumption while ensuring accurate trajectory tracking. The Genetic Algorithm (GA) was used to optimize the control gains, which are essential for the fuzzy controller's performance. The results were tested in the Matlab-Simulink environment, showing improved tracking accuracy, with error reduction in some tasks and successful trajectory following in others that previously could not be completed with an untuned controller. The UAV model used in the study is a multirotor with six degrees of freedom. The model was developed based on the Newton-Euler equations, distinguishing between rotational and translational subsystems. The fuzzy controller was designed to follow a predetermined path, with six controllers used to control the entire system. Four controllers generated control signals, while the remaining torques were used to calculate reference pitch and roll angles. The z-axis controller regulates the height of the UAV, and the error in altitude is used to determine the required power for the rotors. The gains of the fuzzy controller were optimized using a genetic algorithm, which is a metaheuristic algorithm that can find optimal values for these gains. The algorithm was implemented with parameters such as elitism, mutation probability, and crossover probability. The results showed that the tuned controller could complete trajectory tracking tasks that the untuned controller could not. The study also considered disturbances, such as wind gusts, and demonstrated the controller's performance under these conditions. The results indicate that the optimized fuzzy controller significantly improves trajectory tracking performance, making it more effective for UAV applications.This research presents a fuzzy logic controller for an Unmanned Aerial Vehicle (UAV) with gains optimized using a genetic algorithm. The study focuses on adjusting the gains of a fuzzy controller through a metaheuristic algorithm to improve trajectory tracking performance. A typical fuzzy controller was modeled, designed, and implemented using a mathematical model derived from the Newton-Euler methodology. The control objective is to minimize energy consumption while ensuring accurate trajectory tracking. The Genetic Algorithm (GA) was used to optimize the control gains, which are essential for the fuzzy controller's performance. The results were tested in the Matlab-Simulink environment, showing improved tracking accuracy, with error reduction in some tasks and successful trajectory following in others that previously could not be completed with an untuned controller. The UAV model used in the study is a multirotor with six degrees of freedom. The model was developed based on the Newton-Euler equations, distinguishing between rotational and translational subsystems. The fuzzy controller was designed to follow a predetermined path, with six controllers used to control the entire system. Four controllers generated control signals, while the remaining torques were used to calculate reference pitch and roll angles. The z-axis controller regulates the height of the UAV, and the error in altitude is used to determine the required power for the rotors. The gains of the fuzzy controller were optimized using a genetic algorithm, which is a metaheuristic algorithm that can find optimal values for these gains. The algorithm was implemented with parameters such as elitism, mutation probability, and crossover probability. The results showed that the tuned controller could complete trajectory tracking tasks that the untuned controller could not. The study also considered disturbances, such as wind gusts, and demonstrated the controller's performance under these conditions. The results indicate that the optimized fuzzy controller significantly improves trajectory tracking performance, making it more effective for UAV applications.