The automotive electronic system is a typical heterogeneous distributed embedded system. For such a resource-constrained, cost-sensitive system, how to optimize the hardware cost and energy is a hot topic in current research. Meanwhile, industrial safety requirement must be satisfied according to safety standards. To address this complex problem, This study proposes an optimization algorithm, namely hardware cost and energy consumption optimization algorithm (HCECO), which is based on a genetic algorithm combined with simulated annealing and a state-of-the-art scheduling strategy. It aims to reduce the hardware cost and energy consumption of the embedded product while satisfying the hard real-time and reliability requirements of safety-critical applications during the early design phase. The experiment is completed under three real applications. The results demonstrate that the HCECO algorithm can effectively reduce the hardware cost and energy consumption under the hard real-time and reliability constraints.