Recently, Chenxi Sun et al. from Peking University reported the Biomagnetism Evaluation via Simulated Testing (BEST) software ^[1]. This software integrates a simulated current dipole model of the heart's biomagnetic field with a convolutional neural network to assess the diagnostic performance of magnetocardiography (MCG) devices through simulation tests. It provides a novel approach for optimizing the performance of MCG systems and their sensor components. However, the original biomagnetism model in silico simplified the cardiac current dipole, causing discrepancies between the simulated and actual results. In this study, we focus on optimizing the biomagnetism model in the BEST software and introduces an electrocardiographic vector as the new biomagnetism model, offering a more accurate depiction of the cardiac current dipole’s direction and magnitude. By integrating the electrocardiographic vector into the BEST software, we aim to produce spatial magnetic field simulations that more closely align with real cardiac magnetic field distributions. We compared the differences between the original and optimized models in their simulation of cardiac magnetic field distributions. The results demonstrate that the optimized model significantly improves both the accuracy and realism of the magnetic field simulations. Specifically, the magnetic field distribution generated by the electrocardiographic vector better matches theoretical expectations and real-world data in terms of spatial morphology and intensity distribution. In summary, we optimize the biomagentism model of the original BEST software by introducing the electrocardiographic vector. The enhanced accuracy in simulating cardiac magnetic field distributions will contribute to the development of a second-version BEST software, providing a more reliable simulation tool for evaluating the diagnostic performance of MCG systems. Furthermore, it offers valuable support for the performance evaluation of biomagnetic sensors and related research.