The energy sector is subjected to an increase in power demand year after year. This increase in energy demand will continue due to the need for energy for modern societies. The service providers should face this situation smartly using renewable energy sources to avoid the intermittent nature of these sources and the fluctuations in the demand. Demand response programs (DRP) can play a vital role in increasing the correlation between the loads and the available generation from renewable energy sources (RES). Modern power systems recommend the use of distributed generation (DG) sources to reduce the transmission losses, contingency, and cost of energy and to increase the system reliability, efficiency, and power system loadability. Optimal DR use in the DG can further improve its performance which is the main focus of this paper. Here, a novel DRP-DG strategy is introduced to size and allocate the DG units for minimum congestion in IEEE 33-bus benchmark power system and real Indian power system having 17 buses. The active involvement of the consumers in the DRP facilitates the ultimate objective of maximized total social welfare (TSW) with minimized generation costs. To justify the above multi-objective optimization, two stages of heuristic optimization are introduced in which the rules are fired in the primary stage for determining the optimal size of DG. Implementation of DRP for achieving comprehensive objective function is vindicated in the secondary stage. A real-time Indian utility network is considered and a trade-off output is realized using heuristic fuzzy logic (FL) and genetic algorithm (GA). The results obtained from using the proposed strategy proved its superiority in achieving substantial cost reduction and maximum TSW compared to other soft computing techniques.