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The moving window partial least squares (mwPLS) has higher prediction accuracy in the wavelength selection of near infrared spectroscopy, but the runtime of mwPLS is very long on single core serial algorithm. In order to reduce running time for wavelength selection, the task scheduling strategies on multi core were investigated. Taking interval partial least squares (iPLS) as example on the premise of not changing the prediction accuracy of the serial algorithm, the sequential distribution algorithm (SDA), equal space allocation algorithm (ESAA) and sorting allocation algorithm (SAA) were presented to study the effect of task scheduling strategy on the performance of parallel algorithm. The SAA had the best load balance and the highest parallel efficiency among the three task scheduling strategies. Then, based on the 16 cores cloud computing platform, the SAA was applied to the parallel wavelength selection algorithm of synergy interval partial least squares (siPLS), backward interval partial least squares (biPLS) and mwPLS. Comparing with single core serial algorithm, two cores parallel wavelength selection of biPLS and mwPLS with SAA reduced the runtime from 9.22h and 55.51h to 4.98h and 29.03h, and totally 45.99% and 47.70% runtime of biPLS and mwPLS were saved, respectively. The experimental results showed that when considering the parallel efficiency and computational cost condition, the two cores parallel algorithm for the four spectral region selection algorithms had the highest parallel efficiency and cost performance among the 1~16 core parallel algorithm.