Multi-modal optimization is a troublesome problem faced by optimization algorithms. The multiscale quantum harmonic oscillator algorithm (MQHOA) utilizes group statistics strategy to evaluate the state of the population and neglects the individual state. It will lead the particles to be trapped in local optima when addressing multi-modal optimization problems. This paper proposes a modified MQHOA by introducing strict metastability constraints strategy (MQHOA-SMC). The new strategy adopts a joint constraint mechanism to make the particle states mutual constraint with each other. The modified algorithm enhances the ability to find a better quality solution in local areas. To demonstrate the efficiency and effectiveness of the proposed algorithm, simulations are carried out with SPSO2011, ABC, and QPSO on classical benchmark functions and with the newly CEC2013 test suite, respectively. The computational results demonstrate that MQHOA-SMC is a competitive algorithm for multi-modal problems.