Identifying the forces acting on bridges is crucial for enhancing safety and guiding maintenance and design improvements. This study investigates the optimization of sensor placement for moving force identification (MFI), addressing limitations in existing research, which often simplifies sensor placement analyses. An augmented Kalman filter (AKF) is employed for force identification, using acceleration measurement of the bridge. Finite element simulations, conducted using OpenSees, are integrated to generate the dynamic responses of the bridge under moving forces, providing data for the filter and optimization. To optimize sensor placement, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is applied, enabling a multi-objective analysis of trade-offs between sensor count and identification accuracy. Results demonstrate the feasibility of integrating AKF, OpenSees simulations, and NSGA-II for sensor optimization, offering practical strategies for configuring sensors under real-world constraints. The findings highlight the impact of sensor placement on MFI accuracy, showing the balance between sensor count and error reduction. This research contributes to MFI techniques by incorporating modeling with OpenSees and offers insights for sensor configuration design in bridge structure applications.