This article uses BQPhy software to test the performance of quantum-inspired evolutionary optimization (QIEO) over the conventional genetic algorithm (GA) for structural optimization problems. QIEO uses principles of quantum mechanics and doesn’t require any gradient information as it is a population-based method. We use BQPhy software to solve shape optimization problems using QIEO and later compare its efficacy with GA for two optimization problems: compliance minimization and volume minimization. Results highlight that QIEO converges to better fitness value and requires fewer generations to converge when compared with its classical counterpart. This implies that QIEO requires 20–33% lower number of function evaluations when compared with GA, which inherently converges in much less time.