Abstract: Population initialization is very important to the performance of differential evolution. A good initialization method can help in finding better solutions and improving convergence rate. According to our earlier study, uniform design generation can enhance the quality of initial population. In this study, a Uniform-Quasi-Opposition Differential Evolution (UQODE) algorithm is proposed. It uses a two-population mechanism and incorporates uniform design and quasi-opposition initialization method into differential evolution to accelerate its convergence speed and improve the stability. At the same time, an adaptive parameter control technology is adopted to avoid tuning the parameters of DE. The UQODE is compared with other three algorithms of standard Differential Evolution (DE), Opposition-based Differential Evolution (ODE) and Quasi-Oppositional Differential Evolution (QODE). Experiments have been conducted on 14 benchmark problems of diverse complexities. The results indicate that our approach has the stronger ability to find better solutions than other three algorithms especially for higher dimensional problems, in terms of the quality and stability of the final solutions.