Optimization problems have wide applications in engineering, computational science, and bioinformatics. Traditional optimization algorithms often face problems such as premature convergence, local optimum traps, and slow convergence speed in complex environments such as high-dimensional, nonlinear, and multimodal functions. This paper proposes a novel heuristic optimization algorithm based on vaccine development and immunology—the Immuno-Vaccine Inspired Optimization Algorithm (IVIOA). The algorithm analogizes the optimization solution to antigen variants, achieving adaptive search and global exploration of the solution space by simulating the dual-pathway immune response of B cells and T cells, the memory cell enhancement mechanism, and the vaccine dose-response step size mechanism. This paper details the mathematical model of the algorithm, including the antigen generation mechanism, the immune activation formula, the memory cell renewal mechanism, and the nonlinear step size control formula, providing a complete mathematical framework for subsequent theoretical analysis and practical applications. This algorithm not only possesses unique theoretical innovation but also closely integrates with the core principles of vaccine development and immunology, providing a new research perspective for the field of heuristic optimization.