This paper proposes a rapid-converging trajectory optimization method with multiple constraints to solve technical challenges such as getting stuck in local optima, slow convergence, or non-convergence in multi constraint trajectory planning. An optimization model was established with the deviation of the landing point of the aircraft in the longitudinal plane as the optimization objective, and the terminal angle, velocity, and stagnation heat flux density as constraints. The constrained optimization problem was transformed into an unconstrained multi-objective optimization problem using the penalty function method. In order to further increase the convergence speed of the algorithm, the iterative strategy of the intelligent single particle optimization algorithm was improved on the base of the traditional particle swarm optimization algorithm. The reward or punishment of the particle velocity sub vector was expanded according to the fitness function value. The simulation results show that this method has the advantages of global optimality, rapid convergence, and adaptability to multi-objective constraints, then significantly improves the efficiency of trajectory planning.