Growing concerns about the security of supply and environmental impact of fossil fuels are accelerating the efforts towards designing sustainable and resilient energy systems and supply chains. While many industrialized economies face a critical shortage for meeting their ambitious energy transition targets, the differences in between levelized costs of electricity, supply and demand in between different regions demonstrate the potential for a global green energy economy through the trade of green fuels. This potential and need call for advanced mathematical models and methods that capture multiple conflicting objectives and the specific needs of decision makers to help make informed decisions on designing renewable energy supply chains. In this research, we present a novel decision-making framework that integrates a multi-objective optimization model related to cost, environmental impact and transportation risk with a multi-criteria decision-making (MCDM) method at the post-optimization stage to guide decision-makers in navigating complex supply chain planning decisions. Our approach combines a mixed-integer linear programming (MILP) model with a modified AHP with an entropy cut-off step at the post optimization stage to form a structured methodology where the evaluations of decision makers and experts are systematically incorporated to pinpoint prioritized solutions among a set of optimal solutions to guide decision-making. The proposed framework is generalizable to other complex systems requiring confident decision making where there are multiple and often conflicting objectives and trade-offs.