Traditional ant colony optimization (ACO) algorithm may suffer from ‘premature’ when planning the routing of logistics distribution, which results in a low speed of routing scheduling and optimization. In this paper, a novel logistics distribution routing planning algorithm is proposed by a subsequent combination of particle swarm optimization (PSO) and ant colony optimization. The proposed algorithm takes advantages of the strong global search ability and fast search speed of PSO to obtain the suboptimal solution. And then this suboptimal solution is transformed into the increment of initial pheromone in ACO. Finally, the exact solution is achieved via the positive feedback mechanism of ACO. Simulation results demonstrate that the proposed fusion algorithm, compared with ACO, generates the logistics distribution routing quickly and effectively, gains faster optimization speed and better convergence accuracy, and thus controls the cost of logistics distribution more reasonably.