A spatial interpolation method based on GARBF neural network was used to study available zinc in soil. Comparison between GARBF neural network, RBF neural network and Ordinary Kriging interpolation method in fitting capacity and spatial interpolation capacity, with determination coefficient between measured values and predicted values of the training sample set, approximate error and interpolation error of test samples cited as criteria for evaluation. Results show that in terms of the fitting capacity, the three methods applied to the same area under two different sampling schemes (a & b) followed the sequence of GARBF > RBF > Ordinary Kriging. When average absolute error and root mean square error were chosen to judge precision of the interpolation methods, comparison between GARBF and RBF showed that the approximate errors of the training samples were reduced by 0.22 ~ 0.25 in Scheme a and by 0.10 ~ 0.11 in Scheme b, and the interpolation errors of the test samples by 0.13 ~ 0.11 in Scheme a and by 0.02 ~ 0.13 in Scheme b. Comparison between GARBF and Ordinary Kriging showed that the approximation errors of the training samples were reduced by 1.12 ~ 1.40 in Scheme a and by 1.45 ~ 1.88 in Scheme b and the interpolation errors of the test samples by 0.20 ~ 0.24 in Scheme a and by 0.14 ~ 0.32 in Scheme b. So it is obvious that the GARBF neural network is the least in error and the highest in interpolation precision. The GARBF interpolation map reveals that the application of genetic algorithm overcomes the tendency of neural networks to land in local optima and expands the scope of search of spatial information pertaining to soil, thus to a certain extent avoiding a similar problem of “smooth effect” like Ordinary Kriging. The findings of this study could provide a practical analysis tool and decision-making basis for precision fertilization and prevention of soil pollution.