To solve the problem of component grasping path planning, a hybrid optimization algorithm based on ant colony algorithm and tabu search algorithm was proposed to minimize the time. Firstly, the problem of grasping components based on machine vision was defined as Traveling Salesman Problem (TSP) with precedence constraint. Secondly, comprehensive effects of the sizes of components and the grasping and placement process on path planning were analyzed, and the path selection probability and tabu region were improved adaptively. Thirdly, on the one hand, 2-opt local optimization, pheromone punishment and reward mechanism were introduced to improve the search ability of ants; on the other hand, pheromone evaporation factor was improved adaptively to increase the adaptability of ants. Finally, for the basic algorithm and the improved hybrid optimization algorithm, the performance index and grasping time were compared and analyzed by simulation experiment and platform experiment. The experimental results show that, compared with Ant Colony Optimization (ACO) algorithm and Tabu Search (TS) algorithm, the average iteration times of the proposed hybrid optimization algorithm is reduced by about 50%, and other performances are superior to the other algorithms. The results of platform test also show that the hybrid optimization algorithm is superior to random results and basic algorithm, and it can realize component grasping task quickly.