We consider multi-attribute Bayesian optimization, where each feasible design is associated with a vector of attributes that can be evaluated via a time-consuming computer code, and each vector of attributes is assigned a utility according to a decision-maker’s implicit utility function. We propose a sampling policy that maximizes the expected utility of the design chosen by the decision-maker, where her choice is based on the policy’s sampling-based attribute vector estimates. In contrast with existing approaches for multi-attribute optimization that focus on estimating a Pareto frontier, our approach leverages prior information about the decision-maker’s preferences.