With the rapid advancement of autonomous and connected vehicle technology, positioning accuracy has become a critical requirement in navigation systems. Positioning systems such as Global Navigation Satellite System and Inertial Navigation System face challenges in urban environments, including Global Navigation Satellite System signal blockage and the accumulation of temporal errors in Inertial Navigation System. Standard Kalman Filter and adaptive methods like the Adaptive Kalman Filter and Innovation-Based Adaptive Kalman Filter are unable to effectively reduce the impact of outlier data and prevent filter divergence under Global Positioning System signal disturbances. This paper proposes an improved method called the Innovation-Based Adaptive Kalman Filter optimized with a Genetic Algorithm, which consists of two stages: (1) using Improved Adaptive Kalman Filter to adaptively update the measurement noise covariance matrix online and detect outlier data through the Chi-squared test; (2) further optimizing the measurement noise covariance matrix using a Genetic Algorithm. This approach reduces the impact of outlier data and enhances filter stability against environmental noise variations. Simulation results demonstrate that the Improved Innovation Adaptive Kalman Filter with Genetic Algorithm method outperforms Kalman Filter, Adaptive Kalman Filter, and Improved Adaptive Kalman Filter in reducing positioning errors and maintaining filter stability in challenging urban environments. These improvements indicate the capability of Improved Innovation Adaptive Kalman Filter with Genetic Algorithm to enhance positioning accuracy and preserve filter stability in integrated navigation systems.