In the domain of computational intelligence and its applications, how to evaluate the quality of solutions obtained from intelligent optimization algorithms in finite time is an urgent problem to be solved. For continuous optimization problems, an approach is proposed to evaluate the solution quality of intelligent optimization. Firstly, clustering is employed to partition the solution-record. Then, based on the fitness distribution, an alignment probability is calculated as the quality measure. Experiments are performed on uniformly-distributed search, nonuniformly-distributed search, particle swarm optimization and genetic algorithm, respectively. The simulation results show the effectiveness of the proposed method.