Design of embedded systems involves a number of architecture decisions which have a significant impact on its quality. Due to the complexity of today’s systems and the large design options that need to be considered, making these decisions is beyond the capabilities of human comprehension and makes the architectural design a challenging task. Several tools and frameworks have been developed, which automate the search for optimal or near-optimal design decisions based on quantitative architecture evaluations for different quality attributes. However, current approaches use approximations for a series of model parameters which may not be accurate and have to be estimated subject to heterogeneous uncertain factors. We have developed a framework which considers the uncertainty of design-time parameter estimates, and optimizes embedded system architectures for robust quality goals. The framework empowers conventional architecture optimization approaches with modeling and tool support for architecture description, model evaluation and architecture optimization on the face of uncertainty.