The efficient operation of a variable pitch control system is an important basis to ensure the stable power output of wind turbines, optimize the operating conditions, and reduce the mechanical load fatigue. Before the operation of the wind turbine, it is necessary to complete the refined design and tuning of the pitch control system parameters offline. In engineering, these parameters mainly rely on engineers to perform manual tuning through experience knowledge and simulation software. This method has high personnel training and optimization time costs and faces low accuracy and poor consistency. However, traditional proportional-integral-derivative optimization algorithms are limited in the process of intelligent tuning of variable pitch control parameters, and it is easy to fall into local optimality. Therefore, based on the idea of proportional integral and derivative control, this article sets convergent and random controller parameter, and further introduces new control targets, control errors, and Levy flight strategies. An improved proportional-integral-derivative optimization algorithm is proposed. IPIDOA and PIDOA, Harris Hawks optimization, whale optimization algorithm, gray wolf optimizer, particle swarm optimization, and genetic algorithm are tested and verified on 4 single-peak reference functions, 4 multi-peak reference functions, and optimization examples of wind turbine pitch control parameters. The results show that IPIDOA has faster convergence speed, better parameter optimization ability, and stronger optimization stability in multi-class optimization cases. Concurrently, by calculating the time complexity of the IPIDOA and comparing the convergence curves of the algorithms in the parameter optimization research of the wind turbine pitch control system, it shows that the IPIDOA algorithm has excellent computational efficiency.