In order to deal with the complex structure of radial basis function (RBF) neural networks due to the excessive number of hidden layer nodes, we propose a RBF neural network structure optimization algorithm based on an improved genetic algorithm (IGA). The IGA is used to optimize the RBF neural network structure based on orthogonal least squares. We globally optimize the column vectors of the output matrixes of the hidden layers to design a RBF network with better structure based on IGA optimization (IGARBF). The algorithm is applied to a temperature and humidity prediction model for the electronic components storage environment. Results show that compared with the RBF neural network structure based on orthogonal least squares, the number of the hidden layer nodes of IGARBF network is reduced by 34, and the number of training steps is reduced by 44. The errors of temperature and humidity of the prediction model for the electronic components storage environment are smaller, and the fitting accuracy is greater than 0.95, thus having better prediction accuracy.