Objectives: The construction of groundwater monitoring network often has the problems of large investment and high maintenance cost. How to use limited funds to set up a comprehensive, scientific and reasonable monitoring network, select key characteristic indicators to maximize the information of groundwater environment and improve the efficiency of water quality evaluation has become a hot topic. In this paper, Shunping karst water system is taken as the research object. Through the selection of evaluation methods and the optimization of the number of monitoring indicators, the main control factors of water quality in the study area are determined, and the groundwater quality monitoring scheme is optimized. Methods: Based on 49 karst water samples in Shunping karst water system in 2022, the water chemistry and water quality characteristics of the study area were analyzed by statistical analysis, Piper diagram and entropy weight water quality index (EWQI), and the key indicators that can represent the karst water quality in the study area were discussed by coupling stepwise multiple linear regression analysis. Results: (1) The karst water of Shunping karst water system has the characteristics of slightly alkaline and low salinity. The hydrochemical type is mainly HCO3-Ca·Mg type (73.47%). The over-standard indicators were NH3-N, pH, Fe, Mn and F-, and the over-standard rates were 10.20%, 4.08%, 4.08%, 4.08% and 2.04%, respectively. (2) The average EWQI in the study area was 26.33, and the water quality was 'excellent', of which the proportions of excellent and good were 91.84% and 8.16%, respectively. (3) The EWQImin model constructed based on groundwater quality data can well represent the actual EWQI, and the key indicators include NH3-N, Fe, Mn, NO3- and F-, and the determination coefficient (R2) and percentage error (PE) values are 0.986 and 3.88%, respectively. Conclusions: The optimization method of groundwater monitoring index based on EWQI and stepwise multiple linear regression can be used as an important reference for optimization index and provide technical methods for regional groundwater environment management. Acknowledgements: This study was supported by the project funded by Beijing Science and Technology Commission (No. Z221100005222014) and the project funded by Beijing Million Talents Project (No. 2020A55). Keywords: Shunping Karst Water System; Entropy Weight Water Quality Index (EWQI); Stepwise Multiple Linear Regression; Key Indicators; Optimization Method First author: Dai Yindong, female, Master. E-mail: 2962375641 @ qq.com. Corresponding author: Guo Gaoxuan, male, professor-level senior engineer, mainly working on the investigation and research of hydrology, environmental geology and urban geology. E-mail : ggx2008 @ qq.com.