In order to accurately separate the objects from the background in images, a thresholding image segmentation algorithm based on multi-objective particle swarm and artificial bee colony hybrid optimization is proposed. Under the framework of multi-objective optimization, the improved inter-class variance criterion and maximum entropy criterion are used as the fitness functions, and then the two fitness functions are optimized by particle swarm and bee colony hybrid optimization to obtain a set of non-dominated solutions. At the same time, the global optimal solution of particle swarm is introduced into the employed bee phase to update the honey source and the search equation is modified, so as to improve the global and local search abilities in the evolution of the bee colony. Finally, the weighted ratio of between-cluster variation and modified intra-cluster variation is adopted to select an optimal solution from a set of non-dominated solutions. Experimental results show that this algorithm can obtains ideal thresholding segmentation results.