The substitution of missing values, also called imputation, is an important data preparation task for data mining applications. This paper describes a nearest-neighbor method to impute missing values, showing that it can be useful for a clustering genetic algorithm. The proposed nearest-neighbor method is assessed by means of simulations performed in two datasets that are benchmarks for data mining methods: Wisconsin Breast Cancer and Congressional Voting Records. The efficacy of the proposed approach is evaluated both in prediction and clustering scenarios. Empirical results show that the employed imputation method is a suitable data preparation tool.