A linear active disturbance rejection controller (LADRC) method based on a multi-strategy enhanced parrot optimization (GOGS-PO) algorithm was proposed in order to address the issues of poor adaptability and difficult parameter tuning of the LADRC in steer-by-wire system. A simulation model of vehicle steer-by-wire system utilizing LADRC was established. The performance of the parrot swarm intelligence algorithm was enhanced through four improvement strategies. A mated-pair initialization strategy was designed to improve population distribution uniformity and enhance global exploration capability. A dynamic residence factor was introduced to optimize staying behavior and avoid local optima. The golden sine algorithm was used to refine the fear-of-strangers behavior. The leader mechanism of the salp swarm algorithm was integrated to accelerate convergence. Validation using test functions shows that GOGS-PO outperforms comparative algorithms in both convergence speed and accuracy. The LADRC parameters were optimized by using GOGS-PO in order to improve control performance. Simulation experiments showed that the GOGS-PO-based LADRC method reduced front wheel steering angle tracking error, yaw rate, and peak sideslip angle under complex conditions compared with traditional methods, achieving front wheel steering angle tracking accuracy on the order of 0.02°. Statistical significance tests confirmed that the GOGS-PO-LADRC control strategy ranked first across all evaluation metrics with significant advantage, demonstrating that the method enhanced vehicle handling stability and dynamic response performance.