The production process of LTCC substrate is complex and its product quality is sensitive to the process parameters, minor molding defects will affect its functional properties. In this paper, BP neural network and multi-objective genetic algorithm-NSGA-Ⅱ (Non-dominated Sorting Genetic Algorithm Ⅱ) are employed to optimize the process parameters in the laminating and sintering process of LTCC substrate. Based on the orthogonal simulation experimental results of the related process parameters and the three major molding defects of the deformation of the micro channel, dislocation of metal interconnection column and substrate warpage occurred during the process of LTCC substrate molding, the training of the neural networks model is carried out and the neural network prediction models among the three major molding defects and their process parameters are established. Based on that, the multi-objective genetic algorithm was applied to optimize the three defects models for multi objective optimization, then the optimal combination of process parameters are obtained, which are used for the guide of the design and fabrication of the related products.