This paper proposes an adaptive genetic algorithm with local search (pro-aGA) for effectively solving reliability optimization design problems. The pro-aGA has the adaptive scheme which can automatically determine the use of local search and adaptively regulate the rates of crossover and mutation operations in genetic algorithm (GA), during genetic search process. For local search, an improved iterative hill climbing method is used and incorporated into GA loop. The adaptive scheme used in the pro-aGA considers the change of the variances resulting from the various fitness values of population at each generation. For comparing the efficiency of the pro-aGA, i) an conventional GA without any adaptive scheme and local search technique, ii) an conventional GA with an local search technique alone, iii) several adaptive GAs (aGAs) with various adaptive schemes are also presented. These algorithms, including the pro-aGA, are tested and analyzed each other using various reliability optimization design problems. Numerical results by various measures of performance show that the proposed pro-aGA outperforms the conventional algorithms.