Abstract: The adaptability of the currently studied power inspection robot scheduling optimization method cannot meet the convergence principle,resulting in a long-time consumption in the scheduling optimization process. For this reason,this paper studies a new optimization method of power inspection robot job scheduling based on genetic algorithm. By analyzing the scale characteristics, the operating image of the electric power inspection robot is determined, and the offset angle is calculated. Genetic algorithm is introduced to design adaptability function, selection, crossover and variation by coding/decoding. The goal of selection operation is to select superior individuals in the population and gradually increase superior individuals in the population. During the iteration,the result is selected or eliminated according to the individual’s adaptability,and the job scheduling optimization of electric power inspection robot is realized according to the selection result. The experimental results show that the adaptability results of the proposed method under different functions can well meet the convergence principle,and the convergence speed and convergence accuracy are higher than the traditional methods,which can effectively shorten the time spent in the job scheduling process.