DEPSO-Scout is a hybrid optimization algorithm combining Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). The solution convergence is balanced between exploration of PSO and exploitation from DE. The suboptimal solution has reduced by the scout bee property of ABC. DEPSO-Scout outperforms traditional DE, PSO, and ABC. However, in a higher dimension of search space, the accuracy of DEPSO-Scout is maintained while the search speed is significantly decreased. From the experiment, the computational time varies depending on the complexity of the problem. To improve the time-performance of DEPSO-Scout, the parallelization techniques becomes an interest. By modifying the DEPSO-Scout algorithm with the parallel approach, the speed of algorithm significantly improved while the correctness of solutions is maintained. The experiment and analysis of speedup and algorithm efficiency are discussed. The improvement opportunity of Parallel DEPSO-Scout is also discussed in the last section.