Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to their power usage pattern and assists the electric utility in minimizing higher power demand in the duration of higher energy demand intervals. Where, this ultimately leads to carbon emission reduction, electricity monetary cost minimization and maximization of power grid efficiency and sustainability. Nowadays, a large number of the DSM strategies available in existing literature concentrate on house hold appliances scheduling to decrease electricity cost. However, they ignore peak to average ratio (PAR) and consumers delay minimization. In this thesis, we consider a load shifting strategy of DSM, to decrease PAR, delay time and total electricity cost. To gain aforementioned objectives, the crow search algorithm (CSA) and enhanced differential evolution (EDE) are employed. In addition, flower pollination algorithm (FPA), grey wolf optimizer (GWO) and their hybrid i.e., flower-grey wolf optimizer (FGWO) are also used. Moreover, bat algorithm (BA), CSA and their hybrid algorithm i.e., bat-crow search algorithm (BCSA) are also used. For simulation of EDE and CSA, a home with 13 appliances are considered. Furthermore, for the simulation of FPA, GWO, FGWO, BA, CSA, and BCSA, a single home consists of 15 appliances are taken into account. For computing monetary cost, Critical peak pricing (CPP) tariff is employed.