As a new emerging service provider, cloud computing, exhibiting advantages and disadvantages when executing the scientific data flows, is getting more and more attention. One of the main factors that constitute the performance bottleneck is there are many homogeneous and concurrent task packages in cloud. This paper focuses on optimizing the scheduling process in dataflow and transforming the optimization objectives into user metrics (makespan and economic cost) and indicators of cloud systems (network bandwidth, storage constraints and system fairness). An efficient multi-objective game algorithm (MOG) is proposed by formulating the optimization problem as a new cooperative game. The MOG method is able to optimize the user metrics while satisfying the constraint of the system metrics and ensuring the efficiency and fairness of the cloud resources. Comprehensive experiments demonstrate that compared with other related algorithms, the proposed MOG method has obvious advantages in terms of algorithm complexity O(l·K·M) (improvement of magnitude), result quality (optimum in some cases) and system level fairness.