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A Taguchi-based-genetic algorithm (TBGA) is used in an adaptive neuro-fuzzy inference system (ANFIS) to optimize design parameters for surface acoustic wave (SAW) gas sensors. The Taguchi method is used to reduce the number of experiments and collect performance data for an SAW gas sensor. The TBGA has two optimization roles. In the ANFIS, the TBGA selects appropriate membership functions and optimizes both the premise and the consequent parameters by minimizing the performance criterion of the root mean squared error. Another role of the TBGA is optimizing design parameters for an SAW gas sensor. Simulated experimental application of the proposed TBGA-based ANFIS approach showed that, in terms of both resonant frequency shift and precision performance, this systematic design approach obtains far superior results compared with the conventional trial-and-error design methods and other Taguchi-based design methods.