To deal with the fluctuations of strip quality, low production capacity, and excessive energy consumption in continuous annealing production line (CAPL), a new multiobjective multifactorial operation optimization (MO-MFOO) model is established for the CAPL in this paper. Different from previous multiobjective operation optimization (MOO) models in literature that only solve one optimization task at a time, the MO-MFOO in this paper can simultaneously solve multiple optimization tasks, each of which is a MOO. To efficiently solve this model, we propose an adaptive multiobjective, multifactorial differential evolution algorithm (AdaMOMFDE), in which multiple mutation operators are adopted and an adaptive selection strategy for these operators is designed according to their search results to accelerate the convergence speed and improve the robustness. Experimental results on benchmark problems show that AdaMOMFDE is better than the traditional multiobjective multifactorial evolutionary algorithm in the literature. Moreover, experimental results on the practical problems illustrate that the MO-MFOO model based on AdaMOMFDE is superior to the traditional multiobjective operation optimization model that only optimizes one task at a time.