Simultaneous Model Order and Parameter Estimation (SMOPE) is a method of utilizing meta-heuristic algorithm to iteratively determine an optimal model order and parameters simultaneously for an unknown system. SMOPE was originally introduced using Particle Swarm Optimization (PSO). However, the performance was worse than conventional ARX. Hence, the objective of this paper is to introduce a new computational model of the SMOPE which employs multiswarm strategy in original SMOPE to diversify the search moves of meta-heuristic algorithm when searching for the best mathematical model. Experiments are performed on six system identification problems. The obtained results prove that incorporating the multi-swarm approach is a good idea to improve original SMOPE.