Partitioning methodologies such as district metered areas (DMAs) are being applied to the stable maintenance of water distribution network systems in normal conditions such as daily operation and abnormal conditions such as water quality and leakage accidents. However, management and evaluation through the use of existing DMAs generally only have the primary goal of stable water quantity and pressure management. Therefore, the methodology can be limited to achieving the direct effects of water quality parameters such as decreased water age, proper management of residual chlorine, and decreased water quality complaints. This study uses a methodology for determining and prioritizing water quality-oriented Priority Control District Metered Areas (PCDMAs) for stable water quality management to respond to the recent large-scale rusty (red) water crisis in Korea. First, 4 evaluation criteria and 11 evaluation indicators were derived using various water quality-related structured data (water quality measurement data, pipeline data, etc.) and unstructured data (water quality complaints, etc.) based on the Geographic Information System (GIS). A comprehensive prioritization assessment was carried out with multi-criteria decision-making methods based on the analytic hierarchy process. As a result, particular indicators of complaint of water quality and the existence of vulnerable facilities (hospitals, school, etc.) were analyzed as the top five priorities, and it was shown that to be important criteria in determining water quality-oriented PCMDAs. Finally, the proposed methodology was applied to the B metropolitan city of the Republic of Korea, and the evaluation results of all the districts were derived and analyzed. The study shows that the data-based water distribution network PCDMAs selection methodology can be used as a decision-making tool to improve the accuracy and reliability of the operation and management (O&M) of the water distribution operator’s water distribution network. In future research, it will be necessary to evaluate PDDMA with detailed data related to the pipe deterioration (buried environment, the condition of internal/external of the pipe, etc.), which had a significant threshold due to data limitations. And it would be possible to make a real-time evaluation of PCDMA with the real-time water quality test data.