Speed governors have critical importance on hydroelectric power plants, which are adjusted to the rotating speed of hydroelectric generation based on load demand of the grid. The rotating speed is the main factor to balance power generation and load demand. The well-designed controller is needed to control speed governors with high accuracy. A well-defined model is needed to obtain desired control structure. Therefore, in this study, initially, the mathematical model of a hydroelectric power plant is obtained by using physical characteristics of a real-world. Then by using this model and corresponding real-world data, a set of controller parameters is designed by using tuning methodologies based on heuristic optimization algorithms, and their performances are compared with each other and with a classical tuning methodology. Evolutionary-based and nature-inspired-based heuristic optimization algorithms are selected as the tuning algorithms not only to compare the performance of these algorithms with a classical method but also with different origins. The performance of the optimized controller improves the performance of the overall system and helps to get desired performance. The results also indicate that as long as the desired performance criteria are defined as accurate as possible, the performance of the optimization algorithms is acceptable.