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This paper presents a quantitative analysis of evolvability with evolutionary activity statistics in an evolutionary fuzzy system. In general, one can estimate the performance of an evolved fuzzy controller by its fitness. However, it is difficult to explain how its fitness or adaptability has been obtained. Evolutionary activity is used to measure the evolvability of fuzzy rules and explain why salient rules have higher evolvability. A genetic algorithm is used to construct a fuzzy logic controller for a mobile robot in simulation environments. The quantitative analysis shows that sufficient evolvability is maintained during the evolution and that it contributes to the construction of the optimal controller.