Recent developments in process design have focused on establishing optimization-based approaches to support decision-making under uncertainty, but few efforts have been made to study and consider how information regarding this uncertainty affects optimal decisions. In this paper we develop an optimal design framework that, besides integrating process profitability, robustness and quality issues, allows one to decide how much it is worth to spend in research and experimentation for selectively reducing parameter uncertainties and guiding R&D activities. The design problem is thus formulated as a stochastic optimization problem, whose objective function includes an information cost term, leading to the identification of optimal parameter uncertainty levels one should end up with, as well as the corresponding amounts to be spent in R&D. A case study comprising a reactor and heat exchanger system is introduced and provides an illustrative application for the suggested methodology.