A collaborative optimization method for the sintering schedule of ternary cathode materials was proposed under microscopic coupling constraints. An oxygen vacancy concentration prediction model based on microscopic thermodynamics and a growth kinetics model based on neural networks were established. Then, optimization formulations were constructed in three stages to obtain an optimal sintering schedule that minimized energy consumption for different requirements. Simulations demonstrate that the models accurately predict the oxygen vacancy concentrations and grain size, with root mean square errors of approximately 5% and 3%, respectively. Furthermore, the optimized sintering schedule not only meets the required quality standards but also reduces sintering time by 12.31% and keeping temperature by 11.96%. This research provides new insights and methods for the preparation of ternary cathode materials.