In the field of unmanned aerial vehicles (UAVs) mission planning, autonomous route planning in complex 3D environments represents a crucial aspect. To enhance the utilization of valuable information and increase search efficiency when solving path planning problems in 3D environments, a novel UAV route planning algorithm named WG-GWO is proposed with a waypoint guidance and an adaptive evolution strategy. Initially, we introduce a novel waypoint extraction method based on cubic B-spline to harness crucial information from individuals eliminated during the evolutionary process. Subsequently, an adaptive evolutionary direction selection strategy is introduced, which utilizes historical information and a differential operator to improve the search ability. The WG-GWO also incorporates Le´vy flight to prevent premature convergence to local minimums. Simulation results validate the effectiveness of the proposed algorithm, demonstrating that WG-GWO outperforms several existing algorithms in generating feasible routes and achieving faster convergence speeds across various scenarios. These results suggest that WG-GWO holds potential for practical engineering applications.