Soil moisture is a critical component of hydrological processes, and its spatio-temporal distribution depends on many geographical factors (such as elevation, slope, and aspect, etc.). Each of the factors is likely influential over a different scale and to a different degree. Near-surface soil moisture data were collected across a working 10-ha field southwest of Ames, IA in growing seasons of 2004 to 2007. A genetic algorithm is developed to compare geographical factors to the moisture patterns over a range of scales. The genetic algorithm will develop a model in which each factor is computed over a different scale for use in prediction of reference variable. Optimized scales for each parameter are arrived at through successive generations, including crossover and mutation of this evolutionary algorithm. Using this approach, not only are the primary influential relationships uncovered, but the most appropriate scale for comparison to moisture pattern is identified. The results of this analysis can be used to predict the spatio-temporal patterns of soil moisture across a region a priori.