A novel consensus-based particle swarm optimization-guided surrogate-enhanced methodology (ACP-S) is developed to solve expensive black-box optimization (EBBO) problems. The proposed methodology consists of three stages: the global exploration and grouping stage, local exploitation via the surrogate model, and the ranking, refinement, and feedback stage. The methodology only searches a subset of the entire search space that contains high-quality optimal solutions and may include the global optimal solution. The proposed three-stage method is fast and deterministic in computing high-quality optimal solutions. Extensive experimental results demonstrate that this proposed method can obtain high-quality optimal solutions and outperforms several existing methods on small or large EBBO test functions.