Automated optimization of proportional-integral-derivative (PID) parameters in servo control systems is crucial for ensuring stability, reducing noise, and minimizing mechanical wear, yet traditional evolutionary methods like genetic algorithms (GA) and particle swarm optimization (PSO) suffer from slow convergence and potential damage due to random parameter initialization. While offline simulations avoid physical damage, they fail to capture real-world nonlinearities. To overcome these challenges, in this work we propose a hybrid neural-evolutionary optimization algorithm with three key contributions: (1) a control system of servo motor enabling real-time interaction, (2) a neural classifier trained on a control system-generated data to constrain PID parameters to safe bounds, and (3) a genetic algorithm leveraging these constraints to halve the search space and accelerate convergence. Experimental results on SPSH-type servo motors demonstrate zero faults across 450 test runs while reducing mechanical vibrations and maintaining robust performance (0–1500 RPM). By unifying data-driven safety with evolutionary optimization, our framework addresses the triad of industrial requirements: computational efficiency, signal quality, and hardware safety.