Remanufacturing contributes to achieving economical, environmental, and social sustainability, and one of its main steps is disassembly aiming to acquire a set of recyclable and reusable components from end-of-life products. This research considers a multi-objective multi-product disassembly sequence planning problem under uncertain circumstances to realize a trade-off among economic, environmental, and social sustainability. Firstly, a multi-objective chance-constrained programming model is formulized to achieve maximal disassembly profit and minimal noise pollution while satisfying energy consumption requirements and obeying various complex product structures. Secondly, a multi-objective group teaching optimization algorithm combining a stochastic simulation approach is particularly devised to handle the problem. In the designed approach, problem-specific encoding and decoding methods are employed to represent and produce feasible solutions. The stochastic simulation approach is utilized to assess the feasibility and performance of the obtained solutions under uncertain environments. Rank and crowding distance approaches are introduced to realize ability grouping, namely, dividing the population into two groups. Precedence preserving crossover and mutation operators are separately utilized on the two groups to achieve population evolution, and an adaptive local search method is developed to enhance exploitation. Thirdly, comparison experiments on some real-world test problems with different scales are carried out. Through dissecting the experimental results with three performance metrics, it can be observed that the devised approach outperforms its competitors by 9.39%–10.00%, 11.37%–59.86%, and 2.36%–7.73% regarding performance, respectively. The experimental results demonstrate the efficiency and excellence of the devised approach in providing high-quality disassembly schemes for managers and engineers.