The adaptive experimentation methodology has been adopted in visual psychophysical modeling in the pursuit of efficiency in experimental time and cost. The standard scheme only optimizes one design in each experimental stage, although simultaneous optimization of multiple designs per stage can be beneficial, but difficult to implement because of a surge in computation. In this study, we incorporated the adaptive experimentation methodology under a Bayesian framework with differential evolution (DE), an algorithm specialized in multi-dimensional optimization problems to explore the multiple-designs-per-stage approach. By taking advantage of parallel computing, DE is computationally fast. The results showed that the multiple-designs-per-stage scheme resulted in a more stable estimation in the early stages of the parameter estimation.