The assembly sequence planning plays a pivotal role in manufacturing, directly affecting the quality, cost, and product assembly time. It is often considered an NP-hard problem due to the large-scale combinatorial factors involved. This work reports a modified particle swarm optimization (MPSO) approach developed to create an assembly sequence optimizing the cost and time elements. It has been achieved by minimizing the number of tool and orientation changes in the assembly. The risk of premature convergence has been evaded by including one linear and two nonlinear functions (concave and exponential) in the social and cognitive learning parameters. The performance of the proposed MPSO has been compared with GA and basic PSO and found to be superior in terms of average fitness function value and convergence rate. Case studies have demonstrated the efficacy of the method developed. The proposed algorithm has also provided multimodal solutions for the described case studies.