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The optimization study of superconducting quantum interference devices aims to enhance their performance in detecting minute magnetic flux changes. The research established a Python-based simulation framework using the SuperScreen, Numpy, and Matplotlib libraries for device simulation and performance optimization. This framework supports the simulation of various SQUID shapes, including an algorithm developed for polygon modeling. The simulation analyzed the magnetic response of SQUID superconducting films under non-uniform external magnetic fields, calculating the vector magnetic fields inside and outside the films. Additionally, through meshing techniques, continuous physical space was transformed into a computational model, facilitating the solution of physical equations. The simulation results indicate that the optimized SQUID meets the requirements for high-precision magnetic field detection, providing theoretical and technical support for the design of superconducting devices.