Abstract: This study was conducted to attempt to measure the total sugar content of jujube( Ziziphus jujuba cv.Huizao) using near- infrared spectroscopy,the information can be abstracted by partial least- square regression( PLS).In order to select wavelengths of near infrared spectroscopy in the prediction model of partial least squares regression of jujube sugar content detection,correlation coefficients and genetic algorithms and interval partial least squares( GA- si PLS) were used to select wavelength from near infrared spectroscopy in partial least squares regression model. The spectra were divided into twenty intervals,among which four subsets were selected by GA- si PLS to characterize the net signals of jujubes ' saccharinity. Then twelve absorbance values( A) at their characteristic wavelengths were screened out.It showed that the model developed by selecting twelve wavelengths was better than that of full spectrum.the principal factor number reduced by 4 and the root mean square error of prediction of prediction set reduced by 25%,while correlation coefficient of prediction improved 5%. The results showed that this wavelengths selection method for PLS modeling not only simplified and optimized calibration model but also increased the prediction ability of calibration model.Therefore,genetic algorithms are effective and feasible methods applied in developing mutlivariate calibration model based on partial least squares regression.