Ecological models may be very complex due to the large number of physical, chemical, biological processes and variables and their interactions, leading to long simulation times. These models may be used to analyse different management scenarios providing support to decision-makers. Thus, the simultaneous simulation of different scenarios can make the process of analysis and decision quicker, provided that there are mechanisms to accelerate the generation of new scenarios and optimization of the choices between the results presented. This paper presents a new simulation platform EcoSimNet specially designed for environmental simulations, which allows the inclusion of intelligent agents and the introduction of parallel simulators to speed up and optimize the decision-making processes. Experiments were performed using EcoSimNet computational platform with an agent controlling several simulators and implementing a parallel version of the simulated annealing algorithm for optimizing aquaculture production. These experiments showed the capabilities of the framework, enabling a fast optimization process and making this work a step forward towards an agent based decision support system to optimize complex environmental problems. ; Ecological models may be very complex due to the large number of physical, chemical, biological processes and variables and their interactions, leading to long simulation times. These models may be used to analyse different management scenarios providing support to decision-makers. Thus, the simultaneous simulation of different scenarios can make the process of analysis and decision quicker, provided that there are mechanisms to accelerate the generation of new scenarios and optimization of the choices between the results presented. This paper presents a new simulation platform - EcoSimNet - specially designed for environmental simulations, which allows the inclusion of intelligent agents and the introduction of parallel simulators to speed Lip and optimize the decision-making processes. Experiments were performed using EcoSimNet computational platform with an agent controlling several simulators and implementing a parallel version of the simulated annealing algorithm for optimizing aquaculture production. These experiments showed the capabilities of the framework, enabling a fast optimization process and making this work it step forward towards an agent based decision support system to optimize complex environmental problems.