In the early-phase design of complex systems, a model of design performance is coupled with visualizations of competing designs and used to aid human decision-makers in finding and understanding an optimal design. This consists of understanding the tradeoffs among multiple criteria of a “good” design and the features of good designs. Current visualization techniques are limited when visualizing many performance criteria and/or do not explicitly relate the mapping between the design space and the objective space. We present a new technique called Cityplot, which can visualize a sample of an arbitrary (continuous or combinatorial) design space and the corresponding single or multidimensional objective space simultaneously. Essentially a superposition of a dimensionally reduced representation of the design decisions and bar plots representing the multiple criteria of the objective space, Cityplot can provide explicit information on the relationships between the design decisions and the design criteria. Cityplot can present decision settings in different parts of the space and reveal information on the decision → criteria mapping, such as sensitivity, smoothness, and key decisions that result in particular criteria values. By focusing the Cityplot on the Pareto frontier from the criteria, Cityplot can reveal tradeoffs and Pareto optimal design families without prior assumptions on the structure of either. The method is demonstrated on two toy problems and two real engineered systems, namely, the NASA earth observing system (EOS) and a guidance, navigation and control (GNC) system.