Energy Education, Inc. (EEI), a US management consulting firm, specializes in implementing energy conservation programs for schools, universities, and large churches. Similar to many consulting firms, travel expenses are among its largest budget items. Managing consultant travel for minimum cost and in a manner that meets client needs is critical. Typically, a subject matter expert at the company produces a consultant routing and assignment schedule using a labor-intensive, time-consuming, manual process; the schedule produced is usually far from optimal. The objective of our research is to minimize the total cost of consultant travel and staffing. Our models use a cluster-first, route-second methodology. We developed a set-covering binary integer programming heuristic to cluster clients based on geographic location. The relaxed consultant routing and assignment problem is formulated as a mixed-integer linear programming model using cluster locations and demand with consultant skills and availability. In a recent 12-week period, the results of our research reduced EEI costs by 24 percent and provided several qualitative benefits. We conducted sensitivity analysis to provide EEI with improved decision analytics for additional modification of its existing processes and business routines.