Both the Damped Least Squares （DLS） and Genetic Algorithm （GA） are applicable to automatic design of optical systems. Although DLS has a high search efficiency， it is susceptible to falling into local optima traps. Conversely， GA has strong global search capability in the parameter space of optical structures but weak local search capability. To address these challenges， a Correctable Reinforced Search GA （CRSGA） was proposed. Firstly， DLS was introduced after the GA crossover operation to enhance local search capability. Additionally， a correction strategy was introduced to rollback individuals with deteriorated fitness values before the next iteration， thereby achieving corrective evolutionary results. The improvement of two aspects to genetic algorithm enhanced strengths and compensated for weaknesses. Three typical optical system design experiments， including Double Gaussian （DG）， Reversed Telephoto （RT）， and Finite Conjugate Distance Imaging （FCDI）， were conducted to validate the effectiveness of CRSGA. CRSGA outperforms both DLS and GA， and its optimization outcomes are about 8.92%， 12.19%， and 9.39% respectively better than those of commercial optical design software Zemax DLS. In particularly， the optimization outcomes achieve a significant improvement， reaching 99.98%， 94.33%， and 88.45% respectively compared to the Zemax HAMMER algorithm. In conclusion， it is shown that the proposed algorithm is effective for optical system optimization and can be used for automatic optical system design.