Many coastal areas across the United States (U.S.), including the East Coast, West Coast, and Gulf of Mexico, are characterized by a frequent occurrence of natural hazards. At the natural hazard preparedness stage, State authorities advise the population to evacuate the areas expecting potential impacts. In many cases evacuees are trying to use the same evacuation route, which may further cause congestion and significantly delay the evacuation process. Moreover, many coastal areas have a high percentage of vulnerable population groups (e.g., aging adults), who may require additional time to travel from the hazard location to a given emergency shelter. This project proposed a mathematical model for assigning evacuees to evacuation routes and emergency shelters, considering major driver characteristics (e.g., age, gender, racial group, driving experience, marital status, health condition, etc.), evacuation route characteristics (number of travel lanes), driving conditions (time of the day, day of the week), and traffic characteristics (space headway, time headway), with the overall objective to minimize the travel time of evacuees. A set of heuristic algorithms (including the Most Urgent Evacuee First heuristic, the Most Urgent Evacuee Last heuristic, the Most Urgent Evacuee Group First heuristic, and the Most Urgent Evacuee Group Last heuristic) and exact optimization algorithm (CPLEX) were proposed to solve the emergency evacuation optimization problem. The developed mathematical model and solution algorithms were applied to evacuate the population inhabiting Broward County (Florida), which is often impacted by tropical storms. The developed decision support tools are expected to improve the overall effectiveness of emergency evacuation process, and ensure safety of evacuees, including vulnerable population groups.