Autonomous navigation in partially known or unknown environments, such as agricultural fields, poses significant challenges for mobile robots. The effective guidance of these robots is crucial for their successful operation in dynamic settings. Artificial Potential Fields (APFs) are widely employed for this purpose; however, they often lead to issues such as oscillations and local minima, which can hinder the performance. This study proposes an innovative optimization of the parameters of Artificial Potential Fields using a genetic algorithm (GA) to address these limitations. The GA fine-tunes the attractive and repulsive constants of the potential fields, significantly enhancing the navigation performance. Comprehensive simulations were conducted in a dynamic environment, incorporating various static and mobile obstacles to rigorously test the proposed method. The results demonstrate a significant improvement in the robot performance, highlighted by smoother trajectories, reduced collisions, and improved handling of dynamic obstacles. Specifically, the APF-GA method decreased the time to reach the goal from 18.8 to 16.1 seconds and the distance traveled from 7.61 to 6.43 meters. This integration of the genetic algorithm into the APF method not only enhances the smoothness of the trajectory but also increases the navigation safety in complex environments. These promising results have important implications for real-world applications, particularly in agriculture and logistics, paving the way for more efficient robotic systems.