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Focusing on the problems of uncertainty and carbon emissions in the manufacturing process, this paper studies the low-carbon-emission scheduling optimization problem. Firstly, the variations in workpiece processing time and delivery date are selected as the uncertainty factors. A low-carbon-emission scheduling model for uncertain job shops is constructed with the optimization objectives of the time index, carbon emission index, and robustness index. Secondly, an improved third-generation non-dominated sorting genetic algorithm (NSGA-III) is proposed. Based on the original NSGA-III algorithm, this algorithm introduces the state transition algorithm to perform state transformation, neighborhood sampling, selection update, and alternate rotation operations on the parent population, generating new candidate solutions. Finally, the scheduling model and the improved algorithm are applied to a workshop example. Through case study computation and result analysis, the feasibility and effectiveness of the model and algorithm in addressing the low-carbon-emission job shop scheduling problem under uncertainty are further verified.