Component-based approaches are becoming increasingly popular in the areas of adaptive distributed systems, web services, and grid computing. In each case, the underlying infrastructure needs to address a deployment problem involving the placement of application components onto computational, data, and network resources across a wide-area environment subject to a variety of qualitative and quantitative constraints. In general, the deployment needs to also introduce auxiliary components (e.g., to compress/decompress data, or invoke GridFTP sessions to make data available at a remote site), and reuse pre-existing components and data. To provide the flexibility required in the latter case, recently proposed systems such as Sekitei and Pegasus have proposed solutions that rely upon AI planning-based techniques. Although promising, the inherent complexity of AI planning and the fact that constraints governing component deployment often involve non-linear and non-reversible functions have prevented such solutions from generating deployments in resource-constrained situations and achieving optimality in terms of overall resource usage or other cost metrics. This paper addresses both of these shortcomings in the context of the Sekitei system. Our extension relies upon information supplied by a domain expert, which classifies component behavior into a discrete set of levels. This discretization, often justified in practice, permits the planner to identify cost-optimal plans (whose quality improves with the level definitions) without restricting the form of the constraint functions. We describe the modified Sekitei algorithm, and characterize, using a media stream delivery application, its scaling behavior when generating optimal deployments for various network configurations.