With the rapid expansion of space launch infrastructure, modern launch sites face the critical challenge of parallel task scheduling for multiple launch vehicles (LVs) undergoing concurrent testing. The process involves sequential phases—LV arrival, testing, final assembly, transfer, and propellant loading/launch—each requiring dedicated or shared test facilities. Due to variations in LV configurations, certain test areas are mutually exclusive, while others have limited capacity (typically accommodating only one LV at a time). Under these constraints, achieving efficient multimission parallel scheduling to minimize total completion time has become an urgent operational requirement. Analysis of domestic and international research since 2000 reveals that traditional methods, such as dual-code network diagrams, are inadequate for parallel mission planning. Conventional approaches like the critical path method (CPM) and value chain analysis lack robust quantitative capabilities for handling complex resource conflicts. To address these limitations in China’s space launch scheduling, this study proposes a genetic algorithm (GA)-based framework with a dual-layer encoding scheme. The algorithm dynamically adjusts population size and iteration counts based on the number of parallel missions, while the fitness function directly corresponds to the scheduling objective: minimizing mission duration under facility constraints. Case studies demonstrate the method’s efficacy. For a scenario involving five LVs, the algorithm generates optimized parallel schedules in under one minute, significantly outperforming manual dual-code network diagram construction. The proposed approach exhibits notable universality and extensibility: the encoding scheme can be customized to accommodate diverse LV workflows, enhancing practical applicability.