We propose a fast random simulation strategy based on differentially weighted MC. The strategy improves computation efficiency significantly, and guarantees enough calculation accuracy, thus coordinates contradiction between computation cost and computation accuracy. The main idea is based on majorant kernel. It is possible to transfer a traditional coagulation kernel to a majorant kernel through splitting and amplifying slightly. The maximum of majornant kernel is obtained by single looping over all simulation particles. The maximum majornant kernel is used to approximate the maximum coagulation kernel in particle population, and is further used to search coagulation particle pairs randomly with acceptance-rejection method. The waiting time (time-step) for a coagulation event is calculated by summing coagulation kerenls of particle pairs involved in acceptance/rejection processes.. Double looping in normal Monte Carlo simulation is avoided and computation efficiency is improved greatly.