Computer models have been built for the simulation of water distribution systems since the mid-1960s. However, a model needs to be calibrated before it can be used for analysis and operational study of a real system. Model calibration is a vitally important, but time consuming task. Over last two decades, several approaches using optimization techniques have been proposed for model calibrations. Although most of the methods can make the model agree with field observations, few are able to achieve a good level of calibration in terms of determining the correct model parameters (pipe roughness coefficients, junction demands and valve settings). The previously developed methods appear to be lacking versatility for users to accurately specify calibration task given real data for a real system. This paper proposes a comprehensive and flexible framework for calibrating hydraulic network model. Calibration tasks can be specified for a wat er distribution system according to data availability and model application requirements. It allows a user to (1) flexibly choose any combination of the model parameters such as pipe roughness, junction demand and link (pipes, valves and pumps) operational status, (2) easily aggregate model parameters to reduce the problem dimension for expeditious calculation, and (3) consistently specify boundary conditions and junction demand loadings that are corresponding to field data collection. A model calibration is then defined as an implicit nonlinear optimization problem, which is solved by employing a powerful genetic algorithm (GA), a generic search paradigm based on the principles of natural evolution and biological reproduction. Calibration solutions are obtained by minimizing the discrepancy between the model predicted and the field observed values of junction pressures and pipe flows. With this methodology, a modeler can be fully assisted during a calibration process, thus it is possible to achieve a good model calibration with high level of confidence. As a result, calibrated models can be developed for conducting system analysis and operational management. Example application is presented to demonstrate the efficacy and robustness of the genetic-based methodology for calibrating water distribution model.