Sperm Motility Algorithm (SMA), inspired by the human fertilization process, was proposed by Abdul-Raof and Hezam [1] to solve global optimization problems. Sperm flow obeys the Stokes equation or the Schrۤinger equation as its derived equivalent. This paper combines a classical SMA with quantum computation features to propose two novel Quantum-Inspired Evolutionary Algorithms: The first is called the Quantum Sperm Motility Algorithm (QSMA), and the second is called the Improved Quantum Sperm Motility Algorithm (IQSMA). The IQSMA is based on the characteristics of QSMA and uses an interpolation operator to generate a new solution vector in the search space. The two proposed algorithms are global convergence guaranteed population-based optimization algorithms, which outperform the original SMA in terms of their search-ability and have fewer parameters to control. The two proposed algorithms are tested using thirty-three standard dissimilarities benchmark functions. Performance and optimization results of the QSMA and IQSMA are compared with corresponding results obtained using the original SMA and those obtained from three state-of-the-art metaheuristics algorithms. The algorithms were tested on a series of numerical optimization problems. The results indicate that the two proposed algorithms significantly outperform the other presented algorithms.