To solve the problems of heavy workload and low reconstruction accuracy caused by the large number of training samples in the process of spectral reflectance reconstruction, a sample selection method for spectral reflectance reconstruction based on the NSGA-Ⅱ algorithm was proposed. Recently, Liang et al. proposed a new method to select representative samples from many sample data. The spectral reconstruction error was defined as the product of the root mean square error and goodness-of-fit coefficient. Based on this standard, the sample with the smallest spectral reconstruction error was selected as the representative sample for spectral reflectance reconstruction. Inspired by the work of Liang et al. , this method combined with the NSGA-Ⅱ algorithm to select representative samples. First, the polynomial regression algorithm and pseudo-inverse method were used to reconstruct spectral reflectance for all training samples. Then, the NSGA-Ⅱ algorithm was used to set two objective functions. One is the sum of the spectral root mean square error of the required number of representative samples; the other is the sum of the reciprocal of the goodness-of-fit coefficients, which minimizes the values of the two objective functions. All samples with Pareto level 1 selected by NSGA-Ⅱ algorithm are sorted according to the occurrence times of samples from high to low and selected as representative samples from high to low until the required number of representative samples is reached. Suppose the number of representative samples selected from the sample set with Pareto level 1 is less than the required number. In that case,the samples that appear most frequently in the next level and have not been selected are selected until the number of representative samples reaches the demand. The experiment divided 1 269 dull Munsell standard color cards into even color cards and odd color cards according to sample subscript. In the first group of experiments, Munsell odd color cards were used as the whole training samples, and 20 color blocks were randomly selected from Munsell even color cards as the test samples. In the second group of experiments, Munsell even color cards were selected as the whole training samples, and 20 color blocks were randomly selected from Munsell odd color cards as the test samples. The third group of experimental training samples is the same as the first group, and the RC24 color card is the test sample. The proposed method is compared with the three sample selection methods proposed by Mohammadi, Cao and Liang. The experimental results show that the NSGA-Ⅱ algorithm combined with polynomial regression and pseudo-inverse method to select representative samples for spectral reflectance reconstruction is superior to the existing sample selection methods in terms of root mean square error and color difference, and this method is not for a specific system has generality.