To eliminate the shortcomings of low precision and slow convergence in the sales forecasting based on the traditional BP Neural Network (BPNN), this study proposes a new model based on an Improved Immune Genetic Algorithm (IIGA) optimized BP neural network. IIGA presents a new way of population initialization, a regulatory mechanism of antibody concentration, and a design method of adaptive crossover operators and mutation operators. Therefore, the convergence ability and global search ability of IIGA are greatly improved. In addition, IIGA can optimize the initial weights and thresholds of the BP neural network and overcome the drawbacks of output instability of the BP neural network and proneness to local minimum induced by the randomness of network parameters. With the past records of sales figures in a steel enterprise as an example, the BP, IGA-BP and IIGA-BP neural network forecasting models are built with Matlab for simulation comparison. The experiments demonstrate that the precision of the IIGA-BP model is 23.82% higher than that of the BP model and 22.02% higher than that of the IGA-BP model. The IIGA-BP model possesses better generalization about steel sales forecasting and more stable forecasting, with errors basically in the range of -0.25 to 0.25, and its forcasting precision is dramatically improved. The proposed model provides a more effective method for sales forecasting in enterprises.