Anxi Tieguanyin tea was collected as the research materials in this study. In order to find a fast and non-destructive method for rapid quality evaluation of Anxi Tieguanyin tea, the Genetic Algorithm (GA) was applied to wavelength selection befoe it is combined with partial least squares (PLS) to construct PLS and GA-PLS calibration model. The results showed that the PLS model displayed the highest prediction performance after the Fourier transform near-infrared (FT-NIR) spectrum being processed by smoothing, the second derivative and normalized methods. Statistic results with PLS: RC=0.921, RMSEC=0.543, RP=0.913, RMSEP=0.665. NIR spectra ranging from 6　670 to 4　000 cm-1 were selected, and 1　557 data volume for building calibration model were reduced to 408 with Genetic algorithm. Statistic results with GA-PLS: RC=0.959, RMSEC=0.413, RP=0.940, RMSEP=0.587. It has shown that the prediction precision of calibration set and validation set of GA-PLS model is better than those of PLS model. According to the results, it can effectively improve the prediction ability of the model when the Genetic Algorithm (GA) is applied to select the wavelengths in a traditional model which is based on the near infrared spectroscopy combined with partial least squares. It can also achieve the innovation of the methodology. Furthermore, the quality evaluation GA-PLS model provides strong reference and possesses promotional value. In addition, it provides valuable reference and new avenue for improving the standard of detection technology of tea quality in China.