Realistic decision problems are inherently multiobjective and uncertain. To manage both of these complexities, robust multiobjective optimization strives to aid the decision maker in finding a decision which is Pareto efficient and is hedged against the worst-case scenario. In this paper, we present a robustness approach which is grounded in the decision maker’s preferences by allowing the decision maker to define equivalence classes on the set of scenarios. The upshot of this formulation is the ability to measure the tradeoffs between equivalence classes of scenarios, which allows the decision maker to use multicriteria decision analytic tools on an uncertain multiobjective problem in ways previously not possible. After presenting theoretical results, we demonstrate our method on an example inspired by disaster relief.