In this paper we have developed a Geographic Information Systems based model to support cellulosic ethanol plant least-cost location decisions by integrating geographic distribution of biomass in the study area with associated transportation costs. As an initial step of a multi-factor spatial optimization problem, including both feedstock transportation and ethanol distribution cost, the study investigated the influence of feedstock transportation costs on optimal location decisions. To achieve that purpose, the feedstock resources, in this analysis forest biomass and agricultural crop residue, were spatially investigated relative to the road network and potential cellulosic ethanol plant locations in the state of Washington. The flexibility of the model allows spatial manipulation of the data for the least-cost location identifications considering both cumulative and separate types of feedstock utilization scenarios. Study results show that the ethanol plant transportation cost-minimizing location decisions are significantly influenced by the type of the feedstock utilized, and vary depending on the processing plant capacities.