<p>The economic load dispatch problem of microgrid strives to optimize the allocation of total power demand among generating units under specific constraints. Many optimization techniques have been used to solve this problem in power systems; however, achieving the optimal solution is considered difficult due to the involvement of a nonlinear objective function and large search domain. In order to achieve economic load dispatch more quickly and accurately, a novel economic load dispatch method of microgrid based on hybrid slime mould and genetic algorithm (GSMA) is proposed in this paper. Objective function models and their constraints based on wind, photovoltaic, energy storage and fuel power generation are presented. For the early iterations of the method, crossover and mutation of the genetic algorithm are used to increase the diversity of the population. When the number of iterations reaches the threshold, the slime mould algorithm is used to improve the adaptability to complex objective functions. The velocity matrix is introduced to adjust the direction and speed of the individual movement to enhance the searching ability in GSMA. For performance evaluation, GSMA is compared with slime mould algorithm (SMA), grey wolf optimizer (GWO), sparrow search algorithm (SSA), Harris Hawks optimization (HHO), whale optimization algorithm (WOA) and particle swarm optimization (PSO) using standard optimization functions. The experimental results show that GSMA converges to the optimal solution faster than other algorithms. The algorithms are used for economic load dispatch on the simulation test system. The GSMA spends minimum dispatch cost and achieves the best dispatch results compared to other algorithms. It further demonstrates the effectiveness of the new method in solving the economic load dispatch problem of microgrid.</p>