A yaw control strategy based on wind speed segmentation and multi-objective optimization was proposed to address the frequent yawing issues caused by the heterogeneity and instability of wind resources in complex terrains. First, a detailed analysis of the wind resource characteristics and yaw behavior of the wind farm was conducted to determine an appropriate wind speed segmentation scheme for yaw control, ensuring that the turbines can adjust flexibly under different wind speed conditions to achieve optimal operating efficiency. Next, by analyzing the power generation capacity of the yaw system and the fatigue life of the yaw bearing, a yaw control model was constructed with integrated economic benefits and yaw frequency as the optimization objectives. The nondominated sorting genetic algorithm Ⅱ（NSGA-Ⅱ) was then applied to perform a collaborative optimization of the control parameters in the model. Finally, sensitivity analysis was used to determine the optimal control parameters for each wind speed range. Simulation results show that the optimized yaw control strategy fully leverages its advantages in all wind speed ranges, reasonably balancing the relationship between economic benefits and yaw frequency. Particularly under low wind speed conditions, unnecessary yaw actions are significantly reduced, thereby effectively improving the overall economic efficiency of the wind turbines.