Studies of human problem solving have traditionally used deterministic tasks that require the execution of a systematic series of steps to reach a rational and optimal solution. Most real-world problems, however, are characterized by uncertainty, the need to consider an enormous number of variables and possible courses of action at each stage in solving the problem, and the need to optimize the solution subject to multiple interacting constraints. There are reliable individual differences in people's abilities to solve such realistic problems. It also seems likely that people's ability to solve these difficult problems reflects, or depends on, their intelligence. We report on a study of N = 101 adults who completed a series of visual optimization problems (Traveling Salesperson, Minimum Spanning Tree, and Generalized Steiner Tree Problems), as well as a cognitive optimization problem (a version of the Secretary Problem). We also characterized these individuals along three relevant and important cognitive abilities dimensions-fluid ability, visuo-spatial ability, and cognitive processing speed. Modeling of covariance structures indicated that performance on both types of optimization problems relies on general intelligence and raises the possibility that they can be used to assess intelligence.