Distributed energy systems (DER) can produce heating and cooling energy, along with electrical energy, in small local units. High thermodynamic efficiency and primary energy savings are achieved from simultaneous energy production with cogeneration (dual production, heat and electrical energy) or trigeneration (triple production, heating, and cooling) and electrical energy) systems. Energy transmission losses can be significantly reduced with energy production units located near end users. In addition, these systems add flexibility to energy production systems by offering the opportunity to benefit from local renewable energy sources (e.g. solar, wind) and biomass). Mathematical modeling is a frequently preferred approach for the optimal design of DERs. However, as the number of variables increases in these NP-hard network design and assignment models, the model becomes more complex. In the mathematical model, which proposes a general framework for sustainable urban planning that takes into account the infrastructure needs of distributed energy systems, it has been observed that the solution time increases. especially as the number of demand points and coverage distance increases. In this study, a heuristic algorithm is proposed as an alternative to the mathematical model developed for the assignment of regional-scale distributed energy production points and the optimum design of the heating and cooling network. In order to reduce energy distribution losses and transportation costs, the solution results of the mathematical model created by integrating the maximum coverage distance, which also takes into account the largest possible distance between the supply and demand points, into the proposed capacity fixed-cost location selection model, were compared with the results obtained with the heuristic algorithm. In this context, a genetic algorithm-based solution method was developed and the results were evaluated in order to obtain near-optimal solutions in a shorter solution time.