Grain Transportation Optimization Problem (GTOP) is a typical NP-complete problem. In this paper, a mathematical model of GTOP is constructed and a hybrid artificial intelligence optimization algorithm (HAIOA) for GTOP is proposed. In the algorithm, ant colony algorithm (ACA) is introduced into tabu search algorithm (TSA): the optimal solution of ACA used as calculation starting point of TSA can improve the quality of initial solution of TSA; the optimal pheromone of ACA used to guide neighborhood search of TSA can improve the quality of TSA can improve search quality of TSA. In ACA, adaptive expectation heuristic factor and initial solution distance formula are introduced to obtain some better results with certain differences. In addition, the search method of TSA is improved. The simulation results show that compared with other algorithms for GTOP, hybrid artificial intelligence optimization algorithm (HAIOA) has the advantages of less time consuming and better comprehensive performance, which improves the performance of the algorithm.