Band selection can cut down the large amount of hyperspectral data and alleviate the Hughes phenomenon in supervised classification of ground objects. The generation and evaluation of subsets are two key factors for selection algorithm. A hybrid scheme of random search and heuristic search is proposed to generate the band subset. The method embeds the sequential search into the evolution optimization for better ability of the fine tune in local search space and thus behaves well in both global and local cases. In addition, the embed scheme guarantees the validity of solutions for the optimization algorithms. The performance comparisons among the proposed method, hybrid genetic algorithm (HGA), standard genetic algorithm (SGA) and sequential backward floating search (SFFS) are carried out in the experiments on band selection and classification with the hyperspectral data sets. The results show that the proposed method can obtain better subsets according to the evaluation criterion.