We have developed a methodology for wavelength band selection. This methodology can be used in system design studies to provide an optimal sensor cost, data reduction, and data utility trade-off relative to a specific application. The methodology combines an information theory- based criterion for band selection with a genetic algorithm to search for a near-optimal solution. We have applied this methodology to 612 material spectra from a combined database to determine the band locations for 6, 9, 15, 30, and 60- band sets in the 0.42 to 2.5 microns spectral region that permit the best material separation. These optimal bands sets were then evaluated in terms of their utility related to anomaly ddetection and material identification using multi-band data cubes generated from two HYDICE cubes. The optimal band locations and their corresponding entropies are given in this paper. Our optimal band locations for the 6, 9, and 15-band sets are compared to the bands of existing multi-band systems such as Landsat 7, Multispectral Thermal Imager, Advanced Land Imager, Daedalus, and M7. Also presented are the anomaly detection and material identification results obtained from our generalted multi- band data cubes. Comparisons are made between these exploitation results with those obtained from the original 210-band HYDICE data cubes.