In smart grid, the minimum cost for power consumption is attained by scheduling the appliances load. Shifting the appliances load from on-peak to off-peak time reduces the cost in user's bill without compromising the load demand. In this paper, four scheduling algorithms are proposed by hybridising elephant herding optimisation (EHO) with genetic, firefly, bacterial foraging and binary particle swarm optimisation algorithms. Extensive simulations are performed to schedule the home appliances using proposed algorithms with three pricing tariffs: day ahead real-time pricing, inclined block rates and critical peak pricing. The cost efficiency of optimised power consumption is analysed. Results show that more cost is reduced with proposed hybrid algorithms as compared to the unscheduled and state-of-the-art algorithms.