In order to solve the problem of two-dimensional multiobjective optimization of the given cooling path and the target coiling temperature in the rough tuning region of the laminar cooling system of hot rolled strip steel, a multiobjective optimization genetic algorithm based on the feature library and gene reconstruction technology is proposed to lock the optimal opening and closing feature library of the header in the roughing zone. In this algorithm, the intersection of Pareto front faces in previous generations is used to build a feature library, from which the optimal characteristics of header opening and closing are extracted and embedded into the next generation population, which can effectively inhibit the roam and randomness of population evolution. The dynamic competition mechanism is adopted in the feature base, which makes the individual population present more ideal parallel search characteristics in the global optimization space. The random rounding strategy of the feature library guarantees the uniformity of Pareto front plane distribution in space and improves the ability of the system to control the equilibrium of two dimensional multi-objective. Finally, gene reconstruction technology is a powerful engine to drive the algorithm to converge to the global optimal solution group, and it is an effective measure to improve the control accuracy of the system. The simulation program based on MFC is compiled, and the simulation results verify the effectiveness and advancement of the multi-objective optimization strategy.