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Abstract: Data-driven surrogate models that assist with efficient evolutionary algorithms to find the optimal development scheme have been widely used to solve reservoir production optimization problems. However, existing research suggests that the effectiveness of a surrogate model can vary depending on the complexity of the design problem. A surrogate model that has demonstrated success in one scenario may not perform as well in others. In the absence of prior knowledge, finding a promising surrogate model that performs well for an unknown reservoir is challenging. Moreover, the optimization process often relies on a single evolutionary algorithm, which can yield varying results across different cases. To address these limitations, this paper introduces a novel approach called the multi-surrogate framework with an adaptive selection mechanism (MSFASM) to tackle production optimization problems. MSFASM consists of two stages. In the first stage, a reduced-dimensional broad learning system (BLS) is used to adaptively select the evolutionary algorithm with the best performance during the current optimization period. In the second stage, the multi-objective algorithm, non-dominated sorting genetic algorithm II (NSGA-II), is used as an optimizer to find a set of Pareto solutions with good performance on multiple surrogate models. A novel optimal point criterion is utilized in this stage to select the Pareto solutions, thereby obtaining the desired development schemes without increasing the computational load of the numerical simulator. The two stages are combined using sequential transfer learning. From the two most important perspectives of an evolutionary algorithm and a surrogate model, the proposed method improves adaptability to optimization problems of various reservoir types. To verify the effectiveness of the proposed method, four 100-dimensional benchmark functions and two reservoir models are tested, and the results are compared with those obtained by six other surrogate-model-based methods. The results demonstrate that our approach can obtain the maximum net present value (NPV) of the target production optimization problems.