A challenging engineering optimization problem in electrical power generation is the unit commitment problem (UCP). Determining the scheduling for the economic consumption of production assets over a specific period is the premier objective of UCP. This paper presents a take on solving UCP with Binary Slime Mould Algorithm (BSMA). SMA is a recently created optimization method that draws inspiration from nature and mimics the vegetative growth of slime mould. A binarized SMA with constraint handling is proposed and implemented to UCP to generate optimal scheduling for available power resources. To test BSMA as a UCP optimizer, IEEE standard generating systems ranging from 10 to 100 units along with IEEE 118-bus system are used, and the results are then compared with existing approaches. The comparison reveals the superiority of BSMA over all the classical and evolutionary approaches and most of the hybridized methods considered in this paper in terms of total cost and convergence characteristics.