In this paper, we consider the problem of choosing a least-cost path from a graph that is attributed with multiple fuzzy weights. The cost of a path is determined by multiple conflicting objectives that seek to minimize either the total or maximum values of each feature over the length of the path. We present a framework for evaluating paths with various agent preferences. Our method allows the agent to pick any Pareto optimal path and can be used within a larger framework to model decision-making behavior. Our approach is demonstrated on a hand-crafted example problem.