Automatic inspection of concrete bridge health based on wall-climbing robot is an effective way to promote intelligent bridge management and maintenance， moreover reasonable path planning is particularly important for the robot to obtain comprehensive detection data. Aiming at the engineering practical problem of weight limitation of the wall-climbing robot power supply and the difficulty of energy supplement during inspection， the inspection scenarios of bridge components such as main beams and high piers were fully considered， the energy consumption index was taken as the objective function of performance evaluation optimization and corresponding constraint conditions were established， and a full coverage path planning evaluation model was proposed. An Improved Grey Wolf Optimization （IGWO） algorithm was proposed to solve the problem that traditional Grey Wolf Optimization （GWO） algorithm is prone to fall into local optimum. The IGWO algorithm improved the characteristics of initial gray wolf population which was difficult to maintain relatively uniform distribution in the search space by K-Means clustering. The nonlinear convergence factor was used to improve the local development ability and global search performance of the algorithm. Combining with the idea of individual superiority of particle swarm optimization， the position updating formula was improved to enhance the model solving ability of the algorithm. Algorithm simulation and comparison experiment results show that IGWO has better stability compared with GWO， Different Evolution （DE） and Genetic Algorithm （GA）， IGWO reduces energy consumption by 10.2% - 16.7%， decreases iterations by 19.3% - 36.9% and solving time by 12.8% - 32.3%， reduces path repetition rate by 0.23 - 1.91 percentage points， and reduces path length by 1.6% - 11.0%.