The nonlinear relationship between the wind turbine power coefficient, tip speed ratio, and pitch angle makes tuning the Proportional Integral (PI) controller in the Wind Turbine pitch control for optimal control performance a difficult task. The Grey Wolf Optimizer (GWO) is proposed in this paper to tune the PI controller in pitch control of the Double-Fed Induction Generator (DFIG) Wind Turbine (WT) by minimizing power error. The goal is to create a GWO tuned PI pitch control that outperforms Particle Swarm Optimization (PSO) and Genetic Algorithm tuned PI-based pitch controls on the DFIG Wind Turbine. The tuning of the PI controller in pitch control was formulated as a cost function minimization with bounded gains as constraints. In MATLAB, the GWO, PSO, and GA codes were run separately to minimize the cost function while computing the optimal controller gains. The computed PI gains and the Zeigler Nichols (ZN) baseline gains were loaded into the PI controller of the DFIG-based pitch control of WTs connected to the double-circuit distribution line. The GWO was discovered to converge faster than the PSO, whereas the GA was trapped in a local optimum. Furthermore, simulation tests demonstrated that the GWO-based tuning improved the dynamic stability of WT output when compared to its counterpart algorithms. When compared to the PSO, GA, and ZN algorithms, the GWO algorithm provides a faster settling time in the step response of the pitch angle, which can reduce stress on the pitch actuator.