In this paper, a combined biomass-geothermal system, intended to supply heat in low enthalpy areas with an extremely cold climate, is optimized based on a nonlinear optimization methodology. A Multiple Criteria Decision-Making technique is coupled with a two-step optimization to achieve the most exploitable energy with the least pollution and cost possible. Three nonlinear objective functions for optimization with three criteria for decision-making were used to minimize the heat generation cost and pollution for a modeled building in Kuujjuaq, Canada. The biomass-geothermal system is split into two parts, surface, and subsurface parts. Twelve scenarios, including three wood pellet types, in four distance ranges from pellet mills, are first defined. Then, via modeling a building for heat demand analysis, the required heat is yielded. Afterward, in the first step of optimization, the cost and pollution functions for surface parts are developed and optimized using the genetic algorithm and screened by the MCDM technique, called TOPSIS, to size the biomass and geothermal subsystems. In the second step, using the sizing from the first step as a constraint, the cost of the geothermal ground heat exchanger is minimized. Twelve scenarios are optimally configured in this way with minimum cost and pollution in relation to operational parameters, such as utilization time and rated powers. The research proposes a methodology that sizes the biomass geothermal (bio-geo) system and can be extended to other technologies, such as turbines, energy storages, or fuel. Furthermore. It provides a correlation between cost and heat generation from biomass-geothermal systems for Kuujjuaq, Canada, and twelve optimal scenarios with system operating parameters. A basis for system sizing and system selection for baseload and peak demand shaving is also considered. Geothermal- and biomass-rated capacities vary with scenarios from 44% to 56% of the total rated capacity.