An increasing demand of real-time monitoring and data analysis for the power grid, is facing challenges from the processing rate of massive real-time measurement data such as voltage, current and power from billions of sensors. To guarantee process rate, many measurement data scheduling method had been studied, while the problem for the balance of realtime requirement and load balance has not been solved well, which leads to uneven load distribution and data congestion. Thus, this paper proposed a measurement data scheduling method based on genetic algorithm. The proposed method considers the characteristics of historical measurement data, task allocation matrix, and creates an adaptive parameter iterative solution based on using genetic algorithm. The results show that the proposed method can achieve appreciate load balance with time latency guarantee.