With the increasing complexity of mixed-signal chips, the number of test items has also grown significantly. However, rapid production cycles and shortened product lifespans impose stringent constraints on the time available for engineers to refine the testing strategies. Existing methods primarily rely on fully characterized results, particularly from failed chips, making them incompatible with stop-on-first-failure mechanism where complete failure characterization is impractical. Another widely used index, the process capability index, assumes normally distributed results, limiting its applicability for non-normal characteristics. To address these limitations, this paper proposes a fuzzy comprehensive evaluation method. It integrates the extra information gain cost(EIGC), the distribution characteristics of test results, and the proximity to upper and lower limits of metrics, making it applicable to a wider range of test item evaluations. Unlike previous methods, our approach solely utilizes the database of passing chips to compute EIGC, enabling the identification and removal of less informative test items without requiring failed chip data, and therefore suitable for stop-on-first-failure mechanism. The methodology has been validated on the binning processes and final test of two different mixed-signal chips. Experimental results show that the fuzzy comprehensive evaluation method with EIGC information can effectively handle non-normally distributed characteristics, and achieves test-time reductions of 66.23% and 28.12%, respectively, in the chip binning process and final test, while maintaining an extremely low defect-escape rate.Our findings provide a scalable and efficient solution for mixed-signal chip test optimization, reducing reliance on failure characterization and improving test efficiency in high-volume manufacturing environments.