A heuristic task scheduling strategy for intelligent manufacturing in the big data-driven fog computing environment is proposed to address the problem that current resource scheduling and allocation methods in the fog computing environment cannot comprehensively consider the dynamics and uncertainty of resources, resulting in the prolonged delay and high energy consumption. First, a system model with three computing modes for intelligent manufacturing is constructed based on intelligent terminal devices, fog nodes, fog servers, fog gateways, and the cloud. Then, the objective function is optimized by jointly considering the delay matrix, the energy consumption matrix, and the reliability matrix, and corresponding constraints are given based on the selection of computing modes, the decision variables of fog nodes as well as the constraints about the delay. Finally, the intervals of crossover-mutation operators are divided according to the fitness value, and individuals of the population are updated by taking different operations based on the operators in different intervals, so as to achieve an improvement on the traditional genetic algorithms. Meanwhile, a fog resource scheduling algorithm is proposed based on the improved adaptive genetic algorithm. Simulation experiments are conducted to compare and analyze the delay, energy consumption, and reliability of the proposed method with three other methods under the same conditions. The results show that the proposed method has the lowest delay and energy consumption and the highest reliability, with values of 361.3 s, 352.4 J, and 94.6%, respectively, when the number of task requests is 500. The performance is better than the other three comparison methods.