Designers can simulate thousands, if not millions, of design alternatives more cheaply and quickly than ever before with today's computing power; however, the resulting data can overwhelm designers without proper tools to support multi-dimensional data visualization. In this paper, we discuss the use of a multi-dimensional data visualization tool and visual steering commands which allow designers to navigate multi-attribute trade spaces. The novelty in our work is providing designers with a set of visual steering commands to simultaneously explore the trade space and exploit new information and insights as they are gained. Specifically, designers can explore the entire design space (either sampled randomly or manually) or along the entire Pareto front using the Basic Sampler, Point Sampler, and/or Pareto Sampler. Alternatively, they can exploit information they have gained during the exploration process by searching near a specific point of interest or within a region of high preference using the Attractor, Preference Sampler, and/or Guided Pareto Sampler. Examples of each are included in this paper. Meanwhile, a suite of test problems is being formalized to support our trade space exploration - algorithmic development as well as empirical studies involving human decision-makers. This work supports our long-term goal of quantifying the benefits of putting humans back "in-the-loop" during design optimization.