We consider a problem where robots are given a set of task locations to visit with coarsely known distances. The robots must find the task ordering that reduces the overall distance to visit the tasks. We propose an abstraction that models the uncertainty in the paths, and a Markov Decision Process-based algorithm that selects paths that reduces the expected distance to visit the tasks. We also describe a distributed coordination algorithm to resolve path conflicts. We have shown that our task selection is optimal, our coordination is deadlock-free, and have experimentally verified our approach in hardware and simulation.