Visible near infrared spectroscopy combined with genetic algorithm and successive projection algorithm was investigated to detect soil organic matter (OM). A total of 394 soil samples were collected from Wencheng, Zhejiang province. In order to simplify calibration model, a total of 18 characteristic wavelengths were selected with usinggenetic algorithm and successive projections algorithm. These characteristic wavelengths were subjected to partial least squares regression (PLSR) with leave-one-out cross validation to establish calibration model of soil organic matter (OM) with coefficient of determination (R2) of 081, 083, RMSEP of 022, 020 and residual prediction deviation (RPD) of 231, 245 for the calibration set and prediction set respectively. The results showed that using genetic algorithm and successive projections algorithm can simplify the model greatly while the assessing indexes of model such as R2, RMSEP and RPD were not reduced greatly compared with indexes of model using full spectra data to develop calibration model. Therefore, genetic algorithm combined with successive projections algorithm can be used to simply the model to predict soil organic matter.