Investment decision making in transportation asset management is typically characterized by a wide diversity of asset types for purposes of optimization at overall system level. To enhance investment analysis and decision making for these multiobjective problem types, the analysis of trade-offs associated with different performance measures can be illuminating and informative. This paper provides techniques for efficient trade-off analysis as part of multiobjective-optimization for transportation asset management. The multiobjective-optimization problem is first formulated by establishing the objectives expressed in terms of network-level performance measures underlying the analysis of trade-offs. Then, the Extreme Points Nondominated Sorting Genetic Algorithm II (NSGA II) technique, an improvement over traditional NSGA II, is applied to generate Pareto frontiers that illustrate the trade-offs. Using candidate projects from a varied range of asset types as a case study, the paper successfully conducts the trade-offs between performance objectives and cost, and then among the performance objectives. The paper also shows that Extreme Points NSGA II has a faster convergence speed and yields a distribution that is superior to the traditional NSGA II.