This paper presents the development of a Micro Differential Evolution algorithm with self-adaptation ( \(\mu \) SADE) mechanisms for numerical optimization. The proposed \(\mu \) SADE algorithm incorporates self-adaptive mechanisms to update control parameters with the smaller population size to achieve the ability to both explore and exploit. One of the proposed features is the self-adaptive capability to adjust its mutation rate and crossover rate to improve the convergence rate of the needed solution. \(\mu \) SADE was applied to the benchmark functions of complex numerical optimization used by the Congress on Evolutionary Computation (CEC), and the results were statistically compared with those obtained by the classical Differential Evolution (DE), Micro Differential Evolution ( \(\mu \) DE) and the Self-Adaptive Differential Evolution (SADE).