<p>Reservoir injection-production optimization is critical for enhancing oil recovery in high-water-cut mature fields. Polymer flooding improves development efficiency by increasing fluid viscosity and controlling mobility, yet its optimization faces significant challenges: conventional numerical simulation is computationally prohibitive, the incorporation of slug parameters introduces high-dimensional mixed-variable modeling difficulties, and single-stage optimization frameworks frequently converge to local optima in heterogeneous reservoirs. To address these issues, this study proposes a novel Two-Stage Collaborative Optimization (TSCO) framework. First, a hybrid-variable modeling approach is developed, employing binary encoding for slug timing and a Gower-distance-enhanced radial basis function network (RBFNmv) surrogate to map discrete–continuous relationships. Second, a two-stage surrogate-driven architecture is introduced: a global phase uses a dual-mode surrogate search to identify promising regions, followed by a local refinement phase for continuous parameter adjustment, with an adaptive switching mechanism balancing exploration and exploitation. Validation on two benchmark reservoir models demonstrates that TSCO achieves a significant improvement over baseline methods, delivering a net present value about 5% higher than Differential Evolution and converging to high-quality solutions about 22% faster than a conventional surrogate-assisted evolutionary algorithm. The RBFNmv surrogate model shows high predictive accuracy, explaining over 94% of the NPV variance. The key novelty lies in the integrated framework that dynamically handles mixed variables, adaptively switches search stages, and efficiently combines global exploration with local intensification—advancing beyond static, single-strategy surrogate-assisted methods. This work provides a systematic, efficient, and scalable solution for polymer flooding management, offering both methodological innovation and practical insights for field application.</p>