Key strategic decisions in biopharmaceutical portfolio management include drug selection, activity scheduling, and third party involvement. Optimizing strategies is complicated by uncertainty, dependency relationships between decisions, and multiple objectives that may conflict. This paper presents the development of a stochastic combinatorial multiobjective optimization framework designed to address these issues. The framework simulates portfolio management strategies while harnessing Bayesian networks and evolutionary computation concertedly to characterize the probabilistic structure of superior decisions and evolve strategies to multiobjective optimality. This formulation is applied to a case study entailing a portfolio of five therapeutic antibody projects. Optimization was driven by two objectives that conflicted here: maximizing profitability and maximizing the probability of being profitable. Initial analysis of competing strategies along the Pareto optimal front indicated that strategies with clear differences in comprising decisions can compete with similar reward-risk profiles. Hence optimization yielded results that were not intuitive but instead suggested that flexibility between strategies can exist in such large-scale problems. A cluster analysis was used to identify the prevalence of broad and superior building blocks along the Pareto front. In-house development of drugs generally emerged as a preferred constituent of superior strategies which suggested a drive toward minimizing contracting fees, premiums, royalty charges, and losses in sales revenue to third parties; no budgetary constraints were imposed in this case study. It appeared that strategies for scheduling activities had the most overarching impact on performance. Strategies for portfolio structure appeared to have the greatest degree of flexibility relative to other strategic components.