This research analyses the reduction of primary energy consumption (PE)(1) of slab and high-rise housing typologies through the optimization of their building shapes. The process considered applies passive strategies at the concept stage of the architectural design. A quasi-steady state method provides an estimation of building PE consumption in heating and cooling. Then, a multi-objective Genetic Algorithm (GA)(2) optimizes the building shapes with three competitive objective functions: minimization of PE consumption per m(2) (PEI), and maximization of passive volume ratio (PVR)(3), and the sum of roofs and best oriented surfaces (RBOS)(4). The performance-driven design workflow shows the Pareto front solutions that reflect the influence of passive strategies on the building shapes. A performance simulation of the single tower with Energy Plus permits to evaluate the quasi-state method sensibility. Finally, a multivariate linear regression evaluates the interdependence among the optimized parameters. The methodology is tested in four cities with temperate climate: Resistencia and Buenos Aires in Argentina, and Seville and Madrid in Spain. It is suitable for the early stages of design since it allows designers to relate architectural morphologies with environmental issues to reduce energy consumption and Green-House Gasses emissions within the current Climate Change scenario.