In this chapter we propose a novel methodology to optimize the location of the sampling points for a water health monitoring network in an estuary, so that any unknown pathogen pollution episode can be identified -both in intensity and location- from data given by those sampling points. We formulate this control problem as a bi-level optimization one, where the upper-level problem concerns the finding of the optimal sampling locations which best determine a large set of random point-source pollution episodes, and the lower-level problems correspond to the optimal identification of these random pollution sources. In the first case the minimization process is developed through a controlled random search procedure for global optimization, and in the second one by a linked simulation-optimization algorithm. We also show some numerical results for a real-world case in Ría de Vigo (NW Spain).