In this paper we empirically analyze several algorithms for solving a Huff-like competitive location and design model for profit maximization in the plane. In particular, an exact interval branch-and-bound method and a multistart heuristic already proposed in the literature are compared with UEGO (Universal Evolutionary Global Optimizer), a recent evolutionary algorithm. Both the multistart heuristic and UEGO use a Weiszfeld-like algorithm as local search procedure. The computational study shows that UEGO is superior to the multistart heuristic, and that by properly fine-tuning its parameters it usually (in the computational study, always) find the global optimal solution, and this in much less time than the interval branch-and-bound method. Furthermore, UEGO can solve much larger problems than the interval method.