Unmanned Aerial Vehicles (UAVs) have rapidly emerged as a versatile tool in a variety of fields, including surveillance, mapping, agriculture, and military applications. Central to the efficacy of UAVs systems is their ability to maintain stable and efficient flight control, a task typically handled by control algorithms such as Proportional-Integral-Derivative (PID) controllers. However, UAVs face a broad spectrum of dynamic environments, making conventional PID controllers insufficient to address their nonlinearity and uncertainties. For solving these problems, optimization algorithms, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), have become popular tools for tuning PID parameters, improving both control precision and adaptability. This paper provides a review of UAVs control systems that incorporate PID controllers tuned using GA and PSO, focusing on their effectiveness, limitations, and future research directions.