Hybrid concrete (HC) structures are being promoted in China, as well as in other countries. The construction process of HC structures differs from that of traditional cast-in-situ (CiS) structures in two aspects: 1) the construction unit for HC structures is no longer a floor or a construction zone, but a single component, 2) both PC components and CiS components exist in HC structures, and are usually being constructed in parallel. Thus, the optimization of component-level construction schedule considering the construction sequence of different PC and CiS components is necessary for HC structures. However, existing studies about construction sequence optimization or schedule optimization have not taken such aspects into account. To fill the research gaps, this study proposes an approach to optimize the component-level construction schedule for HC structures. An optimization model for the component-level resource-constrained project scheduling problem for HC structures (C-RCPSP-HC) is formulated, involving the precedence relationships and resource constraints, with the objectives of the minimization of the construction makespan and the construction cost. To solve the C-RCPSP-HC model, a multi-objective discrete symbiotic organisms search (MODSOS) is developed to obtain the non-dominated solutions. A case study originated from a real project is conducted to verify the proposed approach. The result shows: 1) the component-level construction schedule optimized by the proposed approach is superior to the traditional construction zone-level construction schedule in eliminating construction conflicts, and contributes to shortening the makespan, decreasing the cost, and enhancing the resource utilization efficiency, 2) the proposed MODSOS has better comprehensive performance than other competitive multi-objective optimization algorithms in solving the C-RCPSP-HC model, as far as convergence, spread, and spacing are concerned. For future improvement, the estimation of the construction durations of activities from historical data will be done by using artificial intelligence methods. Besides, Building Information Modeling will be integrated to automate the data extraction.