In modern electric drive systems, both Direct Current (DC) motors and Permanent Magnet Synchronous Motors (PMSMs) are widely used due to their distinct advantages and applications. DC motors are known for their simplicity and ease of control, making them suitable for various applications requiring precise speed regulation. On the other hand, PMSMs offer higher efficiency, better power density, and improved performance, which are crucial for advanced and demanding applications. This paper attempts to apply the Stochastic Fractal Search (SFS) algorithm to optimize the parameters of the PI controller for both DC motor and PMSM engine speed control and then compare their performance in order to determine which motor functions better in terms of this technique. The SFS technique uses the diffusion feature found in random fractals to find the optimal PI values by minimizing the Integral of Time-weighted Absolute Error (ITAE) to improve the performance of both engines. Our study demonstrates significant improvements in speed control stability, overshoot reduction, faster rise times, lower steady-state errors, and quicker settling times, with the overall performance of the PMSM control system being superior to that of the DC motor. These results show the superiority of the SFS algorithm for PMSM compared to DC motor applications.