In the customer segmentation problem, a large number of features are manually designed and used to comprehensively describe the customer instances. However, some of these features are irrelevant, redundant, and noisy, which are not necessary and effective for customer segmentation. Feature selection is an important data preprocessing method by selecting important features from the original feature set. Particularly, feature selection in customer segmentation is a multiobjective problem that aims to minimize the feature number and maximize the classification performance. This paper proposes a multiobjective feature-selection method based on a meta-heuristic algorithm—hydrological cycling optimization (HCO)—to solve customer segmentation. The proposed method is able to automatically evolve a set of non-dominated solutions that select small numbers of features and achieve high classification accuracy. To this end, three strategies based on the global flow operator, possibility-based acceptance criteria, and density-based evaporation and precipitation are proposed to improve the global search ability and the solution diversity of the proposed approach. The performance of the proposed approach is examined on three customer-segmentation datasets and compared with original multiobjective HCO and six well-known evolutionary multiobjective algorithms. The results confirm the superiority of the proposed approach in solving multiobjective customer-segmentation problems by achieving higher calculation stability, search diversity, and solution quality compared with the other competing methods.