Autonomous sensor planning is a problem of interest to scientists in the fields of computer vision, robotics, and photogrammetry. In automated visual tasks, a sensing planner must make complex and critical decisions involving sensor placement and the sensing task specification. This paper addresses the problem of specifying sensing tasks for a multiple manipulator workcell given an optimal sensor placement configuration. The problem is conceptually divided in two different phases: activity assignment and tour planning. To solve such problems, an optimization methodology based on evolutionary computation is developed. Operational limitations originated from the workcell configuration are considered using specialized heuristics as well as a floating-point representation based on the random keys approach. Experiments and performance results are presented.