We put forward a new attribute outlier detection algorithm based on particle swarm optimization, which applies its simplicity and rapid convergence to detect abnormal attributes. In the process of looking for the optimal value, abnormal attribute candidates are discovered, and they are assessed by the O-Measure fitness. The algorithm’s time complexity is polynomial. Compared with the full search algorithm, the proposed method can greatly reduce search scope; in comparison with the completely random algorithm, it uses heuristic search rules, thus the recall and precision are improved. Experimental results show that the particle swarm algorithm not only has high execution efficiency, but also maintains higher recall and precision rate.