In Community First Responder (CFR) systems, trained volunteers (CFRs) located near patients augment traditional emergency services by responding to alerts via a mobile app, especially for out-of-hospital cardiac arrests (OHCA) where rapid response is crucial. Volunteer efforts can significantly improve survival rates for OHCA. It is important to determine the number of volunteers needed and the recruitment locations to achieve a target performance level. We first model CFR presence using a Poisson point process, allowing us to compute response-time distributions for the first-arriving CFR. Combining this model with known survival rate functions, we deduce survival probabilities for OHCA scenarios. Using convex optimization, we then determine the optimal distribution of CFRs across a region to optimize either the fraction of fast responses or the patient survival rate. This optimal CFR location distribution provides a benchmark for the best possible performance with a given number of volunteers, offering insights into the feasibility of introducing a CFR system in a new region or guiding additional recruitment in existing systems. Additionally, we explore phased alerting policies for CFR systems, where volunteers are notified in stages with time delays, with the goal of maintaining high survival rates while minimizing so-called volunteer fatigue that can arise when more than a required number of volunteers respond to a single OHCA. The policy defining these delays impacts both response times, directly related to survival, and the number of redundant (exceeding the required number) volunteer arrivals. We evaluate the performance of CFR dispatch policies through Monte Carlo simulation. We then present a Markov Decision Process (MDP) formulation and a machine learning-based selection strategy for determining which volunteers to alert and when for each incident, effectively balancing patient survival and volunteer fatigue. We include a case study for both the CFR model and CFR dispatch strategies in Auckland, New Zealand, based on empirical data from their CFR system, GoodSAM. This comprehensive study provides a framework for improving CFR systems globally.