To improve the feature selection stability of evolutionary algorithms, a new method for stable feature selection based on multiobjective ant colony optimization was developed. Feature selection results of three feature ranking methods by resampling policy were combined to provide stable features′ information for multiobjective ant colony optimization; the feature′s Fisher discriminant value and maximal information coefficient value were integrated as heuristic information; the classification correctness rate and value of extensions of Kuncheva similarity measure were taken as two optimization objectives to balance algorithm′s classification performance and its stability. Some comparisons and experiments were carried out on four benchmark data sets, and results show that the proposed method has a better tradeoff between classification performance and feature selection stability.