The typical solar energy potential simulation workflows used in the AEC industry require an abundance of information regarding the detailed geometry and materialization of the design. These requisites render them incompatible with early-stage design decision-making pertaining to form-finding. By performing solar energy potential simulations at the pre-conceptual design phase, the necessary time for later-stage environmental assessments and design improvements is reduced considerably, while building designs with high performance but low environmental footprint are attained. This research proposes a computational framework to navigate voxel-based morphologies of building envelopes in a performative design space. It investigates a generative workflow through an embedded multi-criteria optimization of solar-related indicators, which are mapped in a solar energy potential field. The formulation of this field consists of an a priori assessment of the solar energy potential in every discrete volumetric unit (voxel) and a vectorized description of the interdependency of them. The astronomical size of this solution space renders the use of metaheuristic methods more appropriate. More specifically, a subtractive strategy that incorporates an MCDA approach is being applied in order to reach user-defined optimization targets. The novelty and potential of the proposed methodology lies in streamlining the early decision making process for designers and architects and expanding the morphological possibilities. Through this framework, the performative design space is effectively navigated and nearly optimal solutions are generated, to act as suggestive mechanisms for informed architectural decisions.