Simulated Tempering is a new Monte Carlo method for efficiently simulating systems with rough free energy landscapes, where many coexisting states exist at finite non-zero temperatures. Unlike simulated annealing, where temperature is a static parameter, Simulated Tempering treats temperature as a dynamic variable, allowing the system to remain in equilibrium throughout the process. This method is particularly effective for systems with complex energy landscapes, such as the Random Field Ising Model (RFIM), where conventional methods like Metropolis and cluster algorithms struggle. The method involves defining a larger configuration space with an additional variable, m, which can take multiple values. The probability distribution is then defined in terms of this extended space, allowing the system to explore different temperatures dynamically. This approach enables the system to overcome energy barriers and find global minima more efficiently. In the RFIM, Simulated Tempering significantly improves the efficiency of simulations. It reduces the correlation times for observables not sensitive to magnetization by a factor of 6 compared to conventional methods. It also allows for tunneling between states where the Metropolis method is trapped, providing more accurate estimates of magnetization and energy. The method is implemented by updating the temperature at the end of each lattice sweep, with negligible computational cost. The effectiveness of the method is demonstrated through simulations on the RFIM, where it outperforms traditional methods in terms of both accuracy and efficiency. The results show that Simulated Tempering can significantly reduce the time required to obtain reliable estimates of physical quantities, making it a powerful tool for systems with complex energy landscapes.Simulated Tempering is a new Monte Carlo method for efficiently simulating systems with rough free energy landscapes, where many coexisting states exist at finite non-zero temperatures. Unlike simulated annealing, where temperature is a static parameter, Simulated Tempering treats temperature as a dynamic variable, allowing the system to remain in equilibrium throughout the process. This method is particularly effective for systems with complex energy landscapes, such as the Random Field Ising Model (RFIM), where conventional methods like Metropolis and cluster algorithms struggle. The method involves defining a larger configuration space with an additional variable, m, which can take multiple values. The probability distribution is then defined in terms of this extended space, allowing the system to explore different temperatures dynamically. This approach enables the system to overcome energy barriers and find global minima more efficiently. In the RFIM, Simulated Tempering significantly improves the efficiency of simulations. It reduces the correlation times for observables not sensitive to magnetization by a factor of 6 compared to conventional methods. It also allows for tunneling between states where the Metropolis method is trapped, providing more accurate estimates of magnetization and energy. The method is implemented by updating the temperature at the end of each lattice sweep, with negligible computational cost. The effectiveness of the method is demonstrated through simulations on the RFIM, where it outperforms traditional methods in terms of both accuracy and efficiency. The results show that Simulated Tempering can significantly reduce the time required to obtain reliable estimates of physical quantities, making it a powerful tool for systems with complex energy landscapes.