The expansion of the ethanol industry in Brazil faces two important challenges: to reduce total ethanol production costs and to limit the greenhouse gas (GHG) emission intensity of the ethanol produced. The objective of this study is to economically optimize the scale and location of ethanol production plants given the expected expansion of biomass supply regions. A linear optimization model is utilized to determine the optimal location and scale of sugarcane and eucalyptus industrial processing plants given the projected spatial distribution of the expansion of biomass production in the state of Goias between 2012 and 2030. Three expansion approaches evaluated the impact on ethanol production costs of expanding an existing industry in one time step (one-step), or multiple time steps (multi-step), or constructing a newly emerging ethanol industry in Goias (greenfield). In addition, the GHG emission intensity of the optimized ethanol supply chains are calculated. Under the three expansion approaches, the total ethanol production costs of sugarcane ethanol decrease from 894 US$/m(3) ethanol in 2015 to 752, 715, and 710 US$/m(3) ethanol in 2030 for the multi-step, one step and greenfield expansion respectively. For eucalyptus, ethanol production costs decrease from 635 US$/m(3) in 2015 to 560 and 543 US$/m(3) in 2030 for the multi-step and one-step approach. A general trend is the use of large scale industrial processing plants, especially towards 2030 due to increased biomass supply. We conclude that a system-wide optimization as a marginal impact on overall production costs. Utilizing all the predefined sugarcane and eucalyptus supply regions up to 2030, the results showed that on average the GHG emission intensity of sugarcane cultivation and processing is 80 kg CO2/m(3), while eucalyptus GHG emission intensity is 1290 kg CO2/m(3). This is due to the high proportion of forest land that is expected to be converted to eucalyptus plantations. Future optimization studies may address further economic or GHG emission improvement potential by optimizing the GHG emission intensity or perform a multi-objective optimization procedure. (C) 2016 Elsevier Ltd. All rights reserved.