Setting up a series of water quality monitoring points at important locations of water supply networks is an effective way for monitoring water quality in real time. Most existing layout optimization methods for water quality monitoring points focus on small-scale networks and are difficult to be applied to complex large-scale networks due to inefficient iterations and inferior solutions. Considering the large number of nodes and the high similarity of neighboring nodes in a large-scale water supply network, an optimization solution framework for water quality monitoring point layouts was established to minimize the monitoring time and maximize the coverage ratio of pollution events. The solution framework adopted water quality monitoring point selection models based on node importance indexes of complex networks and comprehensive evaluations of node hydraulic characteristics to simulate pollution events on important nodes of large-scale networks. Meanwhile, an evolutionary strategy of the genetic algorithm was upgraded to improve qualities of solution sets based on node spatial similarity. Simulation experiments on a large-scale network show that the proposed method could solve the problems of low iteration efficiency and low solution quality when the scale of a water supply network was large, so as to obtain an effective layout of water quality monitoring points.