Identifying how much water is being lost from water networks and where the losses are occurring is of great importance to water utilities both for operational and planning reasons as well as for reputation. In this paper, an optimization-based approach is presented for simultaneously quantifying and locating water losses via the process of hydraulic model calibration. The model calibration is formulated as a nonlinear optimization problem that is solved by using a genetic algorithm. The method is developed as an integrated framework of hydraulic simulation and optimization modeling. Case studies are presented to demonstrate how the integrated approach is applied to water loss detection. The results obtained show that the method is effective at detecting water loss as part of the hydraulic calibration of the network model. The accuracy of water loss detection is dependent on the quality of the field observed data and model granularity. However, it has been shown that the approach can be used for reducing the uncertainty of the water loss identification by locating water loss hotspots, which could lead to improved operating revenues at water utilities.