Oligo array is a high throughput technology and is widely used in many scopes of biology and medical researches for quantitative and highly parallel measurements of gene expression. When one faulty step occurs during the synthesis process, it affects all probes using the faulty step. In this thesis, a two-phase genetic algorithm (GA) is proposed to design optimal quality control of oligo array for detecting any single faulty step. The first phase performs the wide search to obtain the approximate solutions and the second phase performs the local search on the approximate solutions to achieve the optimal solution. Besides, the proposed algorithm could hold many non-duplicate individuals and parallelly search multiple regions simultaneously. The superior searching capability of the two-phase GA helps us to find out the 275 nonequireplicate cases that settled by the hill-climbing algorithm. Furthermore, the proposed algorithm also discovers five more open issues.