Groundwater quality assessment often requires a large number of water quality indicators, which not only increases the sampling and testing cost, but also easily causes repeated descriptions and distorted results. In this study, water quality indicators were optimized by Pearson correlation coefficient and factor analysis, and then the groundwater quality of Shouguang City in the dry season of 2017 was evaluated using the fuzzy comprehensive optimization model. The results show that by coupling fuzzy comprehensive evaluation with variable fuzzy sets, water quality categorization can be solved well and the results are more accurate and reasonable. Furthermore, the optimization of indicators reduced the original number down to four, which not only effectively reduced data redundancy, but also retained the effective information of the original indicators, and made it more plausible for the evaluation. On this basis, the main dominant indicators of groundwater pollution in the study area were identified as nitrate, total hardness, zinc and COD. In addition, the groundwater pollution in the vegetable growing area was aggravating, and its over-standard indicators were nitrate and total hardness, which were caused by the excessive use of nitrogen fertilizer and the gradual accumulation of surface pollutants entering the groundwater body through the river.