This research uses multi-objective optimization to determine the optimal mixture of energy and transportation technologies, while optimizing economic and environmental impacts. We demonstrate the added value of using multi-objective mixed integer linear programming (MOMILP) considering economies of scale versus using continuous multi-objective linear programming (MOLP) assuming average cost intervals. This paper uses an improved version to solve MOMILPs exactly (Vincent, et al. 2013). To differentiate optimal solutions with and without subsidies, the impact of policy on the Pareto frontier is assessed. We distinguish between minimizing economic life cycle costs (complete rationality) and required investments (bounded rationality). The approach is illustrated using a Belgian company with demands for electricity and transport. Electricity technologies are solar photovoltaics and the grid; transportation includes internal combustion engine vehicles (ICEVs), grid powered battery electric vehicles (BEVs), and solar powered BEVs. The impact of grid powered BEVs to reduce GHG emissions is limited, yet they are less costly than solar panels to decrease emissions. Current policy measures are found to be properly targeting rational investors who consider life cycle costs, while private (potentially bounded rational) investors often focus on required investments only. ; FWO - postdoctoral mandate 12G5415N