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Many municipalities and large organizations have fleets of vehicles that needto be coordinated for tasks such as garbage collection or infrastructureinspection. Motivated by this need, this paper focuses on the common subproblemin which a team of vehicles needs to plan coordinated routes to patrol an areaover iterations while minimizing temporally and spatially dependent costs. Inparticular, at a specific location (e.g., a vertex on a graph), we assume thecost grows linearly in expectation with an unknown rate, and the cost is resetto zero whenever any vehicle visits the vertex (representing the robotservicing the vertex). We formulate this problem in graph terminology and callit Team Orienteering Coverage Planning with Uncertain Reward (TOCPUR). Wepropose to solve TOCPUR by simultaneously estimating the accumulated cost atevery vertex on the graph and solving a novel variant of the Team OrienteeringProblem (TOP) iteratively, which we call the Team Orienteering Coverage Problem(TOCP). We provide the first mixed integer programming formulation for theTOCP, as a significant adaptation of the original TOP. We introduce a newbenchmark consisting of hundreds of randomly generated graphs for comparingdifferent methods. We show the proposed solution outperforms both the exact TOPsolution and a greedy algorithm. In addition, we provide a demo of our methodon a team of three physical robots in a real-world environment.