In this paper, we propose a network model partition method called QD-PART (Quasi-Dynamic network model PARTition method) for accelerating parallel network simulation. The key of QD-PART is to utilize the fact that a network simulation is typically repeated several times with the same parameter set for estimating the confidence interval of steady state measures. QD-PART gradually optimizes partition of a network model based on past simulation results such as the total simulation time, CPU usage of computing resources, and traffic intensity (i.e., the number of packets transmitted) of each link. At the end of each parallel simulation run, QD-PART re-partitions the network model based on such information aiming at minimizing communication overhead among computing resources and balancing load of sub-network models executed on computing resources. Through several experiments using a parallel-distributed network simulator, we show how parallel network simulation can be accelerated using QD-PART by gradually improving the network model partition.