To improve and simplify the NIR prediction model of the soluble solid content (SSC) of strawberry, backward interval partial least squares (BiPLS) and simulated annealing algorithm (SAA) were combined to select the efficient wavelengths. The strawberry spectra were divided into 21 intervals, among which 4 subsets, i.e. No.8, 13, 16 and 17 were selected by BiPLS. Then SAA was used to select variables in these informative regions, which were used for regression variables of MLR model. Finally, 7565 cm-1, 7706 cm-1, 8289 cm-1, 8489 cm-1, 8499 cm-1, 8724 cm-1 and 8807 cm-1 were used to build a MLR model. The MLR model performs well with root mean standard error of prediction (RMSEP) of 0.428 for SSC, which out performs models using PLS and BiPLS. This work proved that the BiPLS-SAA could determine optimal variables in NIR spectra and improve the accuracy of model.