A sampling design method that seeks chlorine sampling locations using a multiobjective optimization framework is proposed here. The design problem is formulated as a three- and two-objective optimization where the parameter estimation accuracy (F1) is maximized, the the number of sensors is minimized (F2) and the weighted nodal sensitivity is maximized (F3). Trade-off curves are generated using the Strength Pareto Evolutionary Algorithm. The methodology is applied on a real network problem that permits to show a strong relation between F2 and F3 objective functions. Moreover, it is shown that the Pareto front between F1 and F3 is better performed than F1 and F2 trade-off. In addition, the use of F3 objective function trends to spread sensors in entire network.