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Group decision-making (GD) is a fuzzy problem with high complexity and difficult to be handled. Usually the rule-based Group decision-making Support System (GDSS) is used to solve GD problem. But the definition of fuzzy rules and membership functions in GDSS are generally affected by subjective decision. So the rationality of GDSS is difficult to be judged. In this paper, the particle swarm optimization (PSO) algorithm is introduced to improve the fuzzy rule base through optimize the position and shape of fuzzy rule set and weights of rules. A PSO-fuzzy GDSS is set up and used to a real application of vehicle performance evaluation. According to the contrast of three methods: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), non-weighted fuzzy rule base, and PSO-fuzzy GDSS, the result shows that weighted fuzzy rule base after PSO optimized is better than non-weighted fuzzy rule base, and the evaluation values of PSO-fuzzy GDSS are very close to the TOPSIS. Therefore, the PSO-fuzzy GDSS is an efficient method for vehicle performance evaluation and can be applied to more domains.