Designing monitoring programs for detecting potential tracer discharges from unknown locations is challenging. The high variability of the environment may camouflage the anticipated anisotropic signal from a discharge, and there are a number of discharge scenarios. Monitoring operations may also be costly, constraining the number of measurements taken. By assuming that a discharge is active, and a prior belief on the most likely seep location, a method that uses Bayes' theorem combined with discharge footprint predictions is used to update the probability map. Measurement locations with highest reduction in the overall probability of a discharge to be active can be identified. The relative cost between reallocating and measurements can be taken into account. Three different strategies are suggested to enable cost efficient paths for autonomous vessels.