Present-day and future network protocols that include and implement Forward Error Correction are configurable by internal parameters, typically incorporating expert knowledge to set up.We introduce a framework to systematically, objectively and efficiently determine parameters for Random Linear Network Codes (RLNC). Our approach uses an unbiased, consistent simulator in an optimization loop and utilizes a customizable, powerful and extendable parametric loss function. This allows to tailor existing protocols to various use cases, including ultra reliable, low latency communication (URLLC) codes. Successful configurations exploring the search space are under evolutionary pressure and written into a database for instant retrieval. We demonstrate three examples, Full Vector Coding, tail RLNC, and PACE with different focus for each.