Concerning there are a lot of redundant features classified in data which not only affect the classification accuracy, but also reduce classification speed, a feature selection algorithm based on multi-objective Bare-bones Particle Swarm Optimization (BPSO) was proposed to obtain the tradeoff between the number of feature subsets and the classification accuracy. In order to improve the efficiency of the multi-objective BPSO, firstly an external archive was used to guide the update direction of the particle, and then the search space of the particle was improved by a mutation operator. Finally, the multi-objective BPSO was applied to feature selection problems, and the classification performance and the number of selected features of the K Nearest Neighbors ( KNN) classifier were used as feature selection criteria. The experiments were performed on 12 datasets of UCI datasets and gene expression datasets. The experimental results show that the feature subset selected by the proposed algorithm has better classification performance, the maximum error rate of the minimum classification can be reduced by 7.4%, and the maximum execution speed of the classification algorithm can be shortened by 12 s at most.