Cognitive emergency communication networks can meet the requirements of large capacity, high density and low delay in emergency communications. This paper analyzes the properties of emergency users in cognitive emergency communication networks, designs a multi-objective optimization and proposes a novel multi-objective bacterial foraging optimization algorithm based on effective area (MOBFO-EA) to maximize the transmission rate while maximizing the lifecycle of the network. In the algorithm, the effective area is proposed to prevent the algorithm from falling into a local optimum, and the diversity and uniformity of the Pareto-optimal solutions distributed in the effective area are used to evaluate the optimization algorithm. Then, the dynamic preservation is used to enhance the competitiveness of excellent individuals and the uniformity and diversity of the Pareto-optimal solutions in the effective area. Finally, the adaptive step size, adaptive moving direction and inertial weight are used to shorten the search time of bacteria and accelerate the optimization convergence. The simulation results show that the proposed MOBFO-EA algorithm improves the efficiency of the Pareto-optimal solutions by approximately 55% compared with the MOPSO algorithm and by approximately 60% compared with the MOBFO algorithm and has the fastest and smoothest convergence.