Recently permanent magnet direct current motors have become the default choice for a wide range of battery and line power equipment and applications because of their excellent efficiency, quiet operation, small size, high reliability, and low maintenance needs. PID controllers are generally suited for controlling PMDC motor-based systems because of their simplicity in setup, durability, and ease of tuning. For PID controller design and tuning, all conventional methods such as Ziegler–Nichols, Cohen–Coon, Tyreus–Luyben, and internal model control provide initial values for proportional gain (Kp), integral gain (Ki), and derivative gain (Kd); these values are then manually adjusted to achieve the desired performance. The PID controller parameter fine-tuning process is an effort task that requires in-depth domain expertise and knowledge. Soft computing techniques such as genetic algorithm (GA) and particle swarm optimization (PSO) are investigated in this research paper to design PID controllers for brushless PMDC motor control. The results obtained show that PSO outperforms both ZN and GA in minimizing errors and improving dynamic response. PSO optimizes PID gains for minimum error with fast convergence as compared to GA. ZN often fail to provide optimal performance under varying conditions.