Major League Baseball (MLB) is a 30-team North American professional baseball league and Minor League Baseball (MiLB) is the hierarchy of developmental professional baseball teams for MLB. Most MLB players first develop their skills in MiLB, and MLB teams employ scouts, experts who evaluate the strengths, weaknesses, and overall potential of these players. In this dissertation, we study the problem of designing a scouting network for a Major League Baseball (MLB) team. We introduce the problem to the operations research literature to help teams make strategic and operational level decisions when managing their scouting resources. The thesis consists of three chapters that aim to address decisions such as how the scouts should be assigned to the available MiLB teams, how the scouts should be routed around the country, how many scouts are needed to perform the major scouting tasks, are there any trade-off s between the scouting objectives, and if there are any, what are the outcomes and insights. In the first chapter, we study the problem of assigning and scheduling minor league scouts for Major League Baseball (MLB) teams. There are multiple objectives in this problem. We formulate the problem as an integer program, use decomposition and both column-generation-based and problem-specific heuristics to solve it, and evaluate policies on multiple objective dimensions based on 100 bootstrapped season schedules. Our approach can allow teams to improve operationally by finding better scout schedules, to understand quantitatively the strategic trade-offs inherent in scout assignment policies, and to select the assignment policy whose strategic and operational performance best meets their needs. In the second chapter, we study the problem under uncertainty. In reality we observe that there are always disruptions to the schedules: players are injured, scouts become unavailable, games are delayed due to bad weather, etc. We presented a minor league baseball season simulator that generates random disruptions to the scout's schedules and uses optimization based heuristic models to recover the disrupted schedules. We evaluated the strategic benefits of different policies for team-to-scout assignment using the simulator. Our results demonstrate that the deterministic approach is insufficient for evaluating the benefits and costs of each policy, and that a simulation approach is also much more effective at determining the value of adding an additional scout to the network. The real scouting network design instances we solved in the first two chapters have several detailed complexities that can make them hard to study, such as idle day constraints, varying season lengths, off days for teams in the schedule, days where some teams play and others do not, etc. In the third chapter, we analyzed a simplified version of the Single Scout Problem (SSP), stripping away much of the real-world complexities that complicate SSP instances. Even for this stylized, archetypal version of SSP, we find that even small instances can be computationally difficult. We showed by reduction from Minimum Cost Hamiltonian Path Problem that archetypal version of SSP is NP-complete, even without all of the additional complexity introduced by real scheduling and scouting operations.