This thesis is a collection of four submitted manuscripts that present methods to assist forest ecosystem service managers wanting to develop operational sampling, monitoring, and production plans for a set of specific quantifiable ecosystem services, which are formulated as a series of general multi-objective optimization problems. The problems are solved using a heuristic solution technique to determine the best trade-off, efficient, or Pareto frontiers, among the potentially competing and possibly non-commensurate objectives, with the intention that the decision maker(s) will select and implement a single plan from the Pareto frontier. The first manuscript presents the general formulation and solution framework, and demonstrates the method with a problem that has five objectives. The method demonstrates that Pareto frontiers for problems with unknown inputs, many competing objectives, and complex constraints can be analyzed using simple search rules. The second manuscript examines design-based estimation and model-based prediction methods to obtain guesses of unknown inputs, and the resulting outputs, for operational production plans. The results indicate that model-based prediction methods, using simple correlation models, provide benefits by reducing production uncertainties, and thus offer substantial cost savings, or increases in net revenue, when comparison to traditional design-based methods. The third manuscript approximates the Pareto frontier between the maximum information content (i.e. entropy) and the minumum cost for a forest sample, where the results from the sample will be used for many objectives (e.g. prediction, simulation, and optimization). The results depend on the definition of the sample design, but follow similar patterns for all 36 sample designs examined. Finally, the fourth manuscript presents an examination of the Pareto frontier for an operational harvest schedule, using the sample that contains the maximum information content, and the objectives for the operation must satisfy multiple internal and external customers (i.e. production, financial, environmental, logistics, and marketing). By including additional information (i.e. spatial correlation) in the prediction, simulation, and optimization process, these manuscripts demonstrate substantial potential increases in financial objectives (i.e. maximize net revenue, minimize costs), environmental objectives (i.e. maximize unharvested area), materials management objectives (i.e. minimize product degredation), information objectives (i.e. maximum entopy sampling) as well as provide a framework for the objective examination of complex forest ecosystem supply chain problems with multiple objectives.