To deal with the fluctuations of strip quality and excessive energy consumption in continuous annealing production process, this paper firstly establishes a prediction model for strip quality based on data analytics and then builds a multifactorial operation optimization model. The task of the model is to achieve an optimal parameter setting so that the two optimization objectives, which interact but do not conflict with each other, can be optimized simultaneously. To solve this model, an adapive multifactorial evolutionary algorithm(AdaMFEA) is proposed. The algorithm regards different optimization objectives as different categories of factors. The evaluation and search directions of individuals are determined by the information transmission of factors from parents. To imporve the robustness and search efficiency, multiple crossover operators are adopted in the algoirthm and an adaptive selection strategy for these operators is designed according to their search results. Meanwhile, an individual learning strategy based on the backtracking linear search and the quasi-Newton method is also proposed. The experimental results based on the Benchmark problem show that the AdaMFEA can effectively improve the efficiency of the traditional multifactorial evolutionary algorithms(MFEA). The experimental results based on practical industrial problems show that the AdaMFEA can effectively solve the multifactorial operation optimization problem in the continuous annealing production process, and achieve the simultaneous optimization of multiple non-conflicting objectives in the evolution of a population.