With the rapid advancement and widespread application of information and sensor technologies in manufacturing shop floor, the typical challenges that cloud manufacturing is facing are the lack of real-time, accurate, and value-added manufacturing information, the efficient shop floor scheduling strategy, and the method based on the real-time data. To achieve the real-time data-driven optimization decision, a dynamic optimization model for flexible job shop scheduling based on game theory is put forward to provide a new real-time scheduling strategy and method. Contrast to the traditional scheduling strategy, each machine is an active entity that will request the processing tasks. Then, the processing tasks will be assigned to the optimal machines according to their real-time status by using game theory. The key technologies such as game theory mathematical model construction, Nash equilibrium solution, and optimization strategy for process tasks are designed and developed to implement the dynamic optimization model. A case study is presented to demonstrate the efficiency of the proposed strategy and method, and real-time scheduling for four kinds of exceptions is also discussed.